Triple
T13200252
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basic English |
E314221
|
entity |
| Predicate | hasAdditionalTechnicalVocabularySize |
P9908
|
FINISHED |
| Object | 150 words |
—
|
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: 150 words | Statement: [Basic English, hasAdditionalTechnicalVocabularySize, 150 words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAdditionalTechnicalVocabularySize Context triple: [Basic English, hasAdditionalTechnicalVocabularySize, 150 words]
-
A.
hasLimitedVocabulary
Indicates that an entity possesses or uses only a small or restricted set of words or terms in communication or expression.
-
B.
hasKnownVocabulary
Indicates that an entity possesses a defined, identifiable set of terms or words that it can recognize or use.
-
C.
hasDistinctVocabulary
Indicates that one entity’s vocabulary is different or distinguishable from that of another entity.
-
D.
hasNumberOfTerms
chosen
Indicates the quantity of distinct terms or elements associated with a given entity or expression.
-
E.
estimatedNumberOfLanguages
Indicates the approximate count of distinct languages associated with an entity, typically based on estimation rather than an exact measurement.
- 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_69d806aee7308190b70a237ba2a6e3e1 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf054f88190b05ced98d5a22a62 |
completed | April 10, 2026, 11:51 p.m. |
| PD | Predicate disambiguation | batch_69d98bc6bc108190b5a6a265bf6e9fd4 |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:16 p.m.