Triple
T183373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amherst, New York |
E3925
|
entity |
| Predicate | hasCollegeTownCharacter |
P4747
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Amherst, New York, hasCollegeTownCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCollegeTownCharacter Context triple: [Amherst, New York, hasCollegeTownCharacter, true]
-
A.
isCollegeTownOf
chosen
Indicates that a town or city is primarily known for and significantly shaped by the presence of a particular college or university.
-
B.
hasTown
Indicates that one entity possesses, contains, or is associated with a town as part of its structure, jurisdiction, or composition.
-
C.
hasMainCampus
Indicates that an educational institution is primarily based at or chiefly associated with a particular campus location.
-
D.
hasPublicUniversityCampus
Indicates that a public university maintains or operates a campus at the specified location.
-
E.
hasCampusFeature
Indicates that a campus possesses or includes a specific physical or functional feature.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25926be9c8190a4cfce66f57589d1 |
completed | Feb. 28, 2026, 2:55 a.m. |
| PD | Predicate disambiguation | batch_69a2566dfc388190988b1b42d5daaafe |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.