Triple
T4631093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Mountain Lake, New York |
E101417
|
entity |
| Predicate | intersectsAt |
P50696
|
FINISHED |
| Object | Junction of NY 28 and NY 30 |
—
|
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: Junction of NY 28 and NY 30 | Statement: [Blue Mountain Lake, New York, intersectsAt, Junction of NY 28 and NY 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intersectsAt Context triple: [Blue Mountain Lake, New York, intersectsAt, Junction of NY 28 and NY 30]
-
A.
hasCrossingPoint
chosen
Indicates that two or more entities intersect or share at least one common point in space or along their paths.
-
B.
locatedAtIntersectionOf
Indicates that something is situated at the point where two or more paths, roads, or boundaries cross or meet.
-
C.
hasNearbyCrossingPoint
Indicates that one location has a crossing point (such as a bridge, crosswalk, or intersection) situated close to it.
-
D.
hasNotableIntersection
Indicates that two entities intersect or cross at a point that is considered significant or noteworthy in some context.
-
E.
isPointWhere
Indicates that one entity is the specific location or point at which another entity, event, or condition occurs or is defined.
- 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_69bd43d2f1c081908cd4b7ec48ecc73d |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a32d6408190962e60b9bce7560d |
completed | March 20, 2026, 2:31 p.m. |
| PD | Predicate disambiguation | batch_69bd5233cb5081908807e2b150f0ca06 |
completed | March 20, 2026, 1:57 p.m. |
Created at: March 20, 2026, 1:13 p.m.