Triple
T3453982
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asiatic lion |
E72856
|
entity |
| Predicate | bodyCharacteristic |
P31173
|
FINISHED |
| Object | prominent belly fold along the underside |
—
|
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: prominent belly fold along the underside | Statement: [Asiatic lion, bodyCharacteristic, prominent belly fold along the underside]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bodyCharacteristic Context triple: [Asiatic lion, bodyCharacteristic, prominent belly fold along the underside]
-
A.
legCharacteristic
Indicates a characteristic, property, or attribute that specifically pertains to the legs of an entity.
-
B.
hasPhysicalFeature
chosen
Indicates that one entity possesses or exhibits a specific physical characteristic or feature of another entity.
-
C.
biologicalCharacteristic
Indicates that one entity possesses or exhibits a particular biological trait, feature, or property in relation to another.
-
D.
skinCharacteristic
Indicates a relationship where an entity is associated with a particular quality, feature, or condition of its skin.
-
E.
hasFleshColor
Indicates that an entity possesses a particular color or characteristic of its flesh or internal tissue.
- 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_69ad85b12a908190a1d10a6b03b4f8ae |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbaa457fc8190824ca00254e962f9 |
completed | March 8, 2026, 6:06 p.m. |
| PD | Predicate disambiguation | batch_69adae041d588190a84a02bca94adec8 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:16 p.m.