Triple
T892921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pittsburgh, Pennsylvania |
E19280
|
entity |
| Predicate | locatedAtCoordinate |
P1573
|
FINISHED |
| Object | 40.4406°N 79.9959°W |
—
|
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: 40.4406°N 79.9959°W | Statement: [Pittsburgh, Pennsylvania, locatedAtCoordinate, 40.4406°N 79.9959°W]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedAtCoordinate Context triple: [Pittsburgh, Pennsylvania, locatedAtCoordinate, 40.4406°N 79.9959°W]
-
A.
coordinateLocation
chosen
Indicates that an entity is located at, or associated with, a specific geographic coordinate or set of coordinates.
-
B.
locatedAtInteractionPoint
Indicates that an entity is positioned at a specific interaction point where interactions or exchanges are intended to occur.
-
C.
locatedAlong
Indicates that one entity is situated adjacent to, or running beside, the length or course of another linear feature (such as a road, river, or railway).
-
D.
locatedIn
Indicates that one entity exists or is situated within the spatial, administrative, or conceptual boundaries of another entity.
-
E.
location depicted
Indicates that one entity visually represents or shows the place where another entity is situated or occurs.
- 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_69a4939d37188190848be3d426ebc9ae |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ad0304b081908d4c92bb2beadb81 |
completed | March 1, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69a4aa9372e88190b5a9db4afdc045c6 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.