Triple
T972340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Presidency College, Kolkata |
E20970
|
entity |
| Predicate | regionallyInfluential |
P12510
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Presidency College, Kolkata, regionallyInfluential, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionallyInfluential Context triple: [Presidency College, Kolkata, regionallyInfluential, yes]
-
A.
influencesRegion
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
B.
hasRegionalSignificance
chosen
Indicates that something holds particular importance, influence, or relevance within a specific geographic region.
-
C.
regionallyAssociatedWith
Indicates that two entities are connected or related based on sharing the same or overlapping geographic or regional context.
-
D.
regionallyKnownAs
Indicates that an entity is known by a particular name or designation within a specific geographic region.
-
E.
primaryRegionOfPopularity
Indicates the geographic region where something is most widely used, favored, or popular compared to other regions.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b44c38f08190997e141d424e9e04 |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a6aa2c8190aebba71320ab678f |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.