Triple
T20664288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naʼvi |
E507841
|
entity |
| Predicate | hasVocabularySize |
P140973
|
FINISHED |
| Object | several thousand 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: several thousand words | Statement: [Naʼvi, hasVocabularySize, several thousand words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVocabularySize Context triple: [Naʼvi, hasVocabularySize, several thousand words]
-
A.
hasKnownVocabulary
Indicates that an entity possesses a defined, identifiable set of terms or words that it can recognize or use.
-
B.
hasAdditionalGeneralVocabularySize
Indicates the size or amount of extra, non-specialized vocabulary associated with an entity.
-
C.
hasDistinctVocabulary
Indicates that one entity’s vocabulary is different or distinguishable from that of another entity.
-
D.
hasVocabularyFrom
Indicates that one entity’s vocabulary, terminology, or set of terms is derived from, based on, or taken from another entity.
-
E.
hasLimitedVocabulary
Indicates that an entity possesses or uses only a small or restricted set of words or terms in communication or expression.
- F. None of above. chosen
Provenance (4 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_69e0b4c059bc81908ea762cd73ea4424 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2f541bc8190ac7946b91647f2b0 |
completed | April 20, 2026, 11:12 p.m. |
| PD | Predicate disambiguation | batch_69e5c0315f5081908098707c6455e56e |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3caef50819093c8159fe8d6435b |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 11:44 a.m.