Triple
T19586566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riverhead station |
E490120
|
entity |
| Predicate | distanceFromNYCPennStation |
P43953
|
FINISHED |
| Object | 73.3 miles (approximate) |
—
|
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: 73.3 miles (approximate) | Statement: [Riverhead station, distanceFromNYCPennStation, 73.3 miles (approximate)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromNYCPennStation Context triple: [Riverhead station, distanceFromNYCPennStation, 73.3 miles (approximate)]
-
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.
distanceFromHobokenTerminal
Indicates the measured distance between a given location and Hoboken Terminal.
-
D.
distanceFromBaltimorePennStation
Indicates the measured distance between a given location and Baltimore Penn Station.
-
E.
distanceToNewYorkCity
Indicates the spatial distance between a given entity’s location and New York City.
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e640523d10819091320a61456f6437 |
completed | April 20, 2026, 3:03 p.m. |
| PD | Predicate disambiguation | batch_69e514dbdb988190b55931a8138c73e7 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:42 p.m.