Triple
T19540195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Averill Park, New York |
E488876
|
entity |
| Predicate | distanceToAlbanyInMiles |
P72699
|
FINISHED |
| Object | approximately 15 |
—
|
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: approximately 15 | Statement: [Averill Park, New York, distanceToAlbanyInMiles, approximately 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAlbanyInMiles Context triple: [Averill Park, New York, distanceToAlbanyInMiles, approximately 15]
-
A.
distanceToAlbanyNewYork
chosen
Indicates the spatial distance between a given location and Albany, New York.
-
B.
distanceFromAlbury
Indicates the spatial distance between a given entity or location and the place named Albury.
-
C.
distanceToMassenaInMiles
Indicates the numerical distance, measured in miles, between a given entity’s location and Massena.
-
D.
distanceFromSydney
Indicates the spatial distance between a given location and the city of Sydney.
-
E.
distanceToCantonInMiles
Indicates the physical separation between an entity and a specified canton, measured in miles.
- 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_69d8e8db5b6c8190984b61f91981f575 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e63871d00881909ed7371ae5577957 |
completed | April 20, 2026, 2:30 p.m. |
| PD | Predicate disambiguation | batch_69e514c9c00481909b76bda67957e58b |
completed | April 19, 2026, 5:45 p.m. |
Created at: April 10, 2026, 1:41 p.m.