Triple
T17171261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lola (Kinky Boots) |
E416737
|
entity |
| Predicate | resolvesConflictThrough |
P23081
|
FINISHED |
| Object | empathy and understanding |
—
|
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: empathy and understanding | Statement: [Lola (Kinky Boots), resolvesConflictThrough, empathy and understanding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: resolvesConflictThrough Context triple: [Lola (Kinky Boots), resolvesConflictThrough, empathy and understanding]
-
A.
conflictResolvedBy
chosen
Indicates that a conflict or dispute is settled or addressed through the actions, decisions, or intervention of a specified entity or process.
-
B.
basedOnConflict
Indicates that one entity is derived from, influenced by, or structured around a particular conflict involving another entity.
-
C.
conflictedBetween
Indicates being torn or uncertain between two or more options, positions, or commitments.
-
D.
wonConflict
Indicates that one entity has prevailed over another in a conflict, contest, or struggle.
-
E.
conflictResult
Indicates the outcome or consequence that arises from a particular conflict between entities.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3fc097950819095631ee5679e03af |
completed | April 18, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69e3830d2a90819092386717dc56f0e8 |
completed | April 18, 2026, 1:11 p.m. |
Created at: April 10, 2026, 5:37 a.m.