Triple
T25930066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wythoff Nim |
E653413
|
entity |
| Predicate | hasPPositionExample |
P160953
|
FINISHED |
| Object | (6, 10) |
—
|
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: (6, 10) | Statement: [Wythoff Nim, hasPPositionExample, (6, 10)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPPositionExample Context triple: [Wythoff Nim, hasPPositionExample, (6, 10)]
-
A.
hasPPositionExample
chosen
Indicates that an entity is associated with a specific example illustrating its position (P-position) in a given context or structure.
-
B.
hasPPositionDefinition
Indicates that something is associated with or assigned a specific definition of its position or placement.
-
C.
hasPositionOn
Indicates that one entity occupies or holds a specific role, job, or spatial location relative to another entity.
-
D.
hasPositionsFor
Indicates that one entity offers or contains available roles, jobs, or positions for another entity.
-
E.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example 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_69e7ab3eb9b881909c1390690551f868 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f612607c388190ab61d1ac7d18e08d |
completed | May 2, 2026, 3:04 p.m. |
| PD | Predicate disambiguation | batch_69f611a9272881909093360472be832c |
completed | May 2, 2026, 3 p.m. |
Created at: April 22, 2026, 8:36 a.m.