Triple
T14979819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aenobarbus |
E373544
|
entity |
| Predicate | denotesPhysicalTrait |
P31173
|
FINISHED |
| Object | beard color |
—
|
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: beard color | Statement: [Aenobarbus, denotesPhysicalTrait, beard color]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: denotesPhysicalTrait Context triple: [Aenobarbus, denotesPhysicalTrait, beard color]
-
A.
hasPhysicalFeature
chosen
Indicates that one entity possesses or exhibits a specific physical characteristic or feature of another entity.
-
B.
legCharacteristic
Indicates a characteristic, property, or attribute that specifically pertains to the legs of an entity.
-
C.
biologicalCharacteristic
Indicates that one entity possesses or exhibits a particular biological trait, feature, or property in relation to another.
-
D.
physicalCharacteristics
Indicates that one entity has or describes the bodily or material attributes, features, or appearance of another entity.
-
E.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6fcebf481909f72cab577560d82 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a6169b48190a679609febd2d0e3 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:51 a.m.