Triple
T4896870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nemean lion myth |
E109703
|
entity |
| Predicate | skinningMethod |
P60511
|
FINISHED |
| Object | uses lion’s own claws |
—
|
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: uses lion’s own claws | Statement: [Nemean lion myth, skinningMethod, uses lion’s own claws]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: skinningMethod Context triple: [Nemean lion myth, skinningMethod, uses lion’s own claws]
-
A.
fleshTexture
Indicates the tactile quality or surface feel of an entity’s flesh, such as how smooth, firm, soft, or coarse it is.
-
B.
skeleton
Indicates that one entity serves as the basic structural framework or underlying support for another.
-
C.
skeletonFeature
Indicates that one entity is a structural or anatomical skeletal feature or component of another entity.
-
D.
skinThickness
Indicates the measured thickness of an entity’s skin, typically quantifying how thick its outer tissue layer is.
-
E.
limbMorphology
Indicates the structural form, shape, and configuration of an organism’s limbs in relation to its body.
- 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_69bd4410bbf88190aad50d2451c863d6 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd706245e48190a61d573438461c30 |
completed | March 20, 2026, 4:05 p.m. |
| PD | Predicate disambiguation | batch_69bd6c306b188190a08a7856beb76db4 |
completed | March 20, 2026, 3:48 p.m. |
| PDg | Predicate description generation | batch_69bd7060f9988190afdf98eb0a38515d |
completed | March 20, 2026, 4:05 p.m. |
Created at: March 20, 2026, 1:28 p.m.