Triple
T6941742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inverbervie |
E160691
|
entity |
| Predicate | distanceTo |
P350
|
FINISHED |
| Object | Montrose |
E27714
|
NE 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: Montrose | Statement: [Inverbervie, distanceTo, Montrose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montrose Context triple: [Inverbervie, distanceTo, Montrose]
-
A.
Montrose
Montrose is a city in western Colorado known for its access to outdoor recreation, including nearby national parks and scenic mountain landscapes.
-
B.
Montrose
Montrose is a residential neighborhood within the town of Wakefield in Middlesex County, Massachusetts.
-
C.
Montrose
chosen
Montrose is a coastal town in eastern Scotland known for its historic harbor, sandy beach, and surrounding nature reserves.
-
D.
Montrose
Montrose is a vibrant, artsy neighborhood in Houston, Texas, known for its eclectic culture, historic homes, and diverse community.
-
E.
Montrose
Montrose is a small town located in Dane County in the state of Wisconsin, United States.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c6884f3db4819080ad65da69386206 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6da85f43881909549ac26b3db135a |
completed | March 27, 2026, 7:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7515bb4688190a81bef732676eb4e |
completed | March 28, 2026, 3:56 a.m. |
Created at: March 27, 2026, 2:28 p.m.