stanfordnlp/CoreNLP · error · ClassCastException

cannot be cast into a

Error message

${object.getClass()} cannot be cast into a ${KBPStatisticalExtractor.class}

What it means

KBPAnnotator's constructor loads a statistical relation-extraction model and requires the deserialized object to be either a LinearClassifier (wrapped into a KBPStatisticalExtractor) or an already-built KBPStatisticalExtractor. If the model file deserializes to any other class, a ClassCastException is thrown naming the actual and expected types.

Solutions

1. Point the KBP statistical model property at the correct serialized model from the matching corenlp models jar (kbp models zip)
2. Verify the model file deserializes to LinearClassifier or KBPStatisticalExtractor (inspect with ObjectInputStream in a scratch program)
3. Ensure the CoreNLP and model archive versions match (e.g. both from the same release)
4. Re-download the kbp models archive and check checksums to rule out corruption

Example fix

// before
props.setProperty("kbp.stat_model", "models/ner-model.ser.gz");
// after
props.setProperty("kbp.stat_model", "edu/stanford/nlp/models/kbp/kbp_statistical_model.ser.gz");

try (ObjectInputStream in = new ObjectInputStream(new GZIPInputStream(new FileInputStream(modelPath)))) {
  Object o = in.readObject();
  if (!(o instanceof LinearClassifier) && !(o instanceof KBPStatisticalExtractor))
    throw new IllegalArgumentException("Not a KBP statistical model: " + o.getClass());
}

static boolean isKbpModel(Object o) {
  return o instanceof LinearClassifier || o instanceof KBPStatisticalExtractor;
}

try {
  pipeline = new StanfordCoreNLP(props);
} catch (ClassCastException e) {
  if (e.getMessage().contains("cannot be cast into a")) {
    log.severe("Wrong KBP model file: " + e.getMessage());
    // point kbp model property at the correct .ser.gz
  } else throw e;
}

When it happens

Trigger: Setting the kbp.stat_extractor (or equivalent model path) property to a file that is not a serialized KBP statistical model — e.g. a different classifier, a generic model, or a corrupt/mismatched file.

Common situations: Pointing KBP model properties at the wrong serialized model (e.g. an NER or sentiment classifier); CoreNLP/kbp-models version mismatch where the model class changed; downloading partial or wrong model archives.

Understand the failure class

Background: "is not a compatible type" / "cannot merge" errors: when a value's type doesn't match what the library requires — this error's family across 65 libraries.

Related errors

• cannot be cast into a…
• First line of input file should be header definition
• Could not parse CoNLL file
• Gabor sucks at logic and he should feel bad about it
• Wanted LexicalizedParser, got

AI-assisted analysis of stanfordnlp/CoreNLP@1b7edd19c4 (2026-09-10). Data as JSON: /api/errors/9f2298b6fe673a50. Report an issue: GitHub.

Appendix: source

Thrown at src/edu/stanford/nlp/pipeline/KBPAnnotator.java:127

      ArrayList<KBPRelationExtractor> extractors = new ArrayList<>();
      // add tokensregex rules
      if (!tokensregexdir.equals(NOT_PROVIDED))
        extractors.add(new KBPTokensregexExtractor(tokensregexdir, VERBOSE));
      // add semgrex rules
      if (!semgrexdir.equals(NOT_PROVIDED))
        extractors.add(new KBPSemgrexExtractor(semgrexdir,VERBOSE));
      // attempt to add statistical model
      if (!model.equals(NOT_PROVIDED)) {
        log.info("Loading KBP classifier from: " + model);
        Object object = IOUtils.readObjectFromURLOrClasspathOrFileSystem(model);
        KBPRelationExtractor statisticalExtractor;
        if (object instanceof LinearClassifier) {
          //noinspection unchecked
          statisticalExtractor = new KBPStatisticalExtractor((Classifier<String, String>) object);
        } else if (object instanceof KBPStatisticalExtractor) {
          statisticalExtractor = (KBPStatisticalExtractor) object;
        } else {
          throw new ClassCastException(object.getClass() + " cannot be cast into a " + KBPStatisticalExtractor.class);
        }
        extractors.add(statisticalExtractor);
      }
      // build extractor
      this.extractor = new KBPEnsembleExtractor(extractors.toArray(new KBPRelationExtractor[0]));
      // set maximum length of sentence to operate on
      maxLength = Integer.parseInt(props.getProperty("kbp.maxlen", "-1"));
    } catch (IOException | ClassNotFoundException e) {
      throw new RuntimeIOException(e);
    }

    // set up map for converting between older and new KBP relation names
    relationNameConversionMap = new HashMap<>();
    relationNameConversionMap.put("org:dissolved", "org:date_dissolved");
    relationNameConversionMap.put("org:founded", "org:date_founded");
    relationNameConversionMap.put("org:number_of_employees/members", "org:number_of_employees_members");
    relationNameConversionMap.put("org:political/religious_affiliation", "org:political_religious_affiliation");
    relationNameConversionMap.put("org:top_members/employees", "org:top_members_employees");

View on GitHub (pinned to 1b7edd19c4)