Triple
T7680654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lycaon |
E173983
|
entity |
| Predicate | distinguishingBodyFeature |
P31173
|
FINISHED |
| Object | slenderBodyLongLegs |
—
|
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: slenderBodyLongLegs | Statement: [Lycaon, distinguishingBodyFeature, slenderBodyLongLegs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distinguishingBodyFeature Context triple: [Lycaon, distinguishingBodyFeature, slenderBodyLongLegs]
-
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.
eyeCharacteristic
Indicates a relationship where an entity possesses a specific attribute, feature, or quality of its eyes.
-
D.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
-
E.
anatomicalFeature
Indicates that one entity is an anatomical part, structure, or feature of another entity.
- 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_69c6995840408190a19de6c51090f46f |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7048b0b448190889bd40e0a38e51a |
completed | March 27, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69c701618d3481908be84b76f36ac5a1 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 4:01 p.m.