Triple
T34800642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto–Montreal |
E1003208
|
entity |
| Predicate | airTravelTimeRangeHours |
P1526
|
FINISHED |
| Object | 1 to 1.5 |
—
|
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: 1 to 1.5 | Statement: [Toronto–Montreal, airTravelTimeRangeHours, 1 to 1.5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airTravelTimeRangeHours Context triple: [Toronto–Montreal, airTravelTimeRangeHours, 1 to 1.5]
-
A.
flightDuration
chosen
Indicates the length of time that a specific flight takes from departure to arrival.
-
B.
flightDurationSeconds
Indicates the length of time, measured in seconds, that a particular flight or flight segment lasts.
-
C.
travelTimeToAirport
Indicates the amount of time required to travel from a given location to an airport.
-
D.
missionDurationHoursApprox
Indicates an approximate length of time, measured in hours, that a mission is expected to last or has lasted.
-
E.
maximumFlightDuration
Indicates the longest allowable or observed time period that a flight can continuously operate.
- 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_69f76db543808190b188c6c86a91491b |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f77a89c4e88190a048e95d42b4a084 |
completed | May 3, 2026, 4:40 p.m. |
| PD | Predicate disambiguation | batch_69f7795b1abc8190823664d1caa94649 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.