Triple
T5338854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babylon station |
E123893
|
entity |
| Predicate | distanceFromNewYorkPennStation |
P43953
|
FINISHED |
| Object | approximately 36 miles |
—
|
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 36 miles | Statement: [Babylon station, distanceFromNewYorkPennStation, approximately 36 miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromNewYorkPennStation Context triple: [Babylon station, distanceFromNewYorkPennStation, approximately 36 miles]
-
A.
distanceFromPennStation
chosen
Indicates the physical distance between a given location and Penn Station.
-
B.
distanceFromGrandCentral
Indicates the spatial distance between a given entity and Grand Central.
-
C.
distanceToNewYorkCity
Indicates the spatial distance between a given entity’s location and New York City.
-
D.
distanceToPoughkeepsie
Indicates the spatial distance between a given entity and the location of Poughkeepsie.
-
E.
distanceToPhiladelphia
Indicates the spatial distance between a given entity’s location and the city of Philadelphia.
- 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_69bd464b07f8819095aa76577c9829e4 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85e86edc81908d87933db6489f91 |
completed | March 20, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69bd845a62b081909782863865b257a9 |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2 p.m.