Triple
T31395140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenneth Hale |
E800843
|
entity |
| Predicate | languageProficiencyClaim |
P741
|
FINISHED |
| Object | was reported to speak or have worked with dozens of languages |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: was reported to speak or have worked with dozens of languages | Statement: [Kenneth Hale, languageProficiencyClaim, was reported to speak or have worked with dozens of languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageProficiencyClaim Context triple: [Kenneth Hale, languageProficiencyClaim, was reported to speak or have worked with dozens of languages]
-
A.
claimsAboutLanguage
Indicates that one entity makes statements or assertions regarding the properties, use, status, or characteristics of a language.
-
B.
eligibleLanguage
Indicates that a particular language satisfies the required conditions to be considered valid or allowed in a given context.
-
C.
hasWorkedInLanguage
Indicates that an entity has performed work or professional activities using a particular language.
-
D.
languagesSpoken
chosen
Indicates that an entity is able to communicate using one or more specified languages.
-
E.
languageOfExpression
Indicates that a particular language is used as the medium or form in which an expression (such as a text, utterance, or work) is realized.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f224ea9998819086ae2e4f4f4091c8 |
completed | April 29, 2026, 3:34 p.m. |
| NER | Named-entity recognition | batch_69ff7ae5d088819089aa3b6360b6b749 |
completed | May 9, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69ff7a4df6488190bf60d675b36b1d6d |
completed | May 9, 2026, 6:17 p.m. |
Created at: April 29, 2026, 9:19 p.m.