Triple
T38628557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Denver Airport station |
E937382
|
entity |
| Predicate | travelTimeToUnionStation |
P193040
|
FINISHED |
| Object | approximately 37 minutes |
—
|
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 37 minutes | Statement: [Denver Airport station, travelTimeToUnionStation, approximately 37 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelTimeToUnionStation Context triple: [Denver Airport station, travelTimeToUnionStation, approximately 37 minutes]
-
A.
commuterRailTravelTimeToManhattan
Indicates the travel time by commuter rail required to reach Manhattan from a given location.
-
B.
distanceFromGrandCentral
Indicates the spatial distance between a given entity and Grand Central.
-
C.
travelTimeToAirport
Indicates the amount of time required to travel from a given location to an airport.
-
D.
drivingTimeFromNewYorkCity
Indicates the amount of time it takes to drive from New York City to a specified location.
-
E.
distanceFromPennStation
Indicates the physical distance between a given location and Penn Station.
- F. None of above. chosen
Provenance (4 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_69f76ed5ca3c81909288f61fbf37b359 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd35d108908190b79b1e8e6bbd62aa |
completed | May 8, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69fd34cb46108190b43c3b7f67ec4cd4 |
completed | May 8, 2026, 12:56 a.m. |
| PDg | Predicate description generation | batch_69fd35d029588190a525aa8a506e7708 |
completed | May 8, 2026, 1:01 a.m. |
Created at: May 3, 2026, 4:32 p.m.