Triple
T12990919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King of Queens |
E321902
|
entity |
| Predicate | featuresRegionalInfluence |
P19397
|
FINISHED |
| Object | West African pop |
—
|
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: West African pop | Statement: [King of Queens, featuresRegionalInfluence, West African pop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresRegionalInfluence Context triple: [King of Queens, featuresRegionalInfluence, West African pop]
-
A.
influencesRegion
chosen
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
B.
hasRegionalInfluenceFrom
Indicates that one entity’s influence, impact, or authority in a region is derived from or shaped by another entity.
-
C.
impactRegion
Indicates the geographic or spatial area that is affected or influenced by a particular event, action, or phenomenon.
-
D.
regionOfCulturalImpact
Indicates the geographic area where an entity’s cultural influence, activities, or effects are most significantly felt or observed.
-
E.
cityOfInfluence
Indicates the city that significantly shapes, impacts, or exerts influence over a given 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dbdd94c8190ac4bbecca02dc77b |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:43 p.m.