Triple
T7176556
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Istanbul derby |
E167333
|
entity |
| Predicate | includesMatchup |
P38133
|
FINISHED |
| Object | Beşiktaş vs Fenerbahçe |
—
|
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: Beşiktaş vs Fenerbahçe | Statement: [Istanbul derby, includesMatchup, Beşiktaş vs Fenerbahçe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesMatchup Context triple: [Istanbul derby, includesMatchup, Beşiktaş vs Fenerbahçe]
-
A.
matchOf
Indicates that one entity is a specific match, counterpart, or corresponding instance of another entity within a defined context or set.
-
B.
includesMatch
chosen
Indicates that one entity contains or encompasses a particular match or matching instance of another entity.
-
C.
definingMatchup
Indicates a matchup that is especially significant or decisive in defining the outcome, narrative, or importance of a broader event or competition.
-
D.
includesCompetition
Indicates that one entity contains, features, or encompasses a competition as part of its scope or content.
-
E.
matchupFrequency
Indicates how often a particular pair or set of entities are matched or paired against each other within a given context or timeframe.
- 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_69c68889a2748190a316c5e65360361a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e9b045c48190b27b2d6f7c11026f |
completed | March 27, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69c6e74fb0f48190b2ad4dd4efdd241a |
completed | March 27, 2026, 8:23 p.m. |
Created at: March 27, 2026, 2:49 p.m.