Triple
T20530808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Los Angeles–San Francisco |
E504060
|
entity |
| Predicate | hasTravelTimeByCar |
P89543
|
FINISHED |
| Object | about 6 hours via Interstate 5 (typical) |
—
|
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: about 6 hours via Interstate 5 (typical) | Statement: [Los Angeles–San Francisco, hasTravelTimeByCar, about 6 hours via Interstate 5 (typical)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTravelTimeByCar Context triple: [Los Angeles–San Francisco, hasTravelTimeByCar, about 6 hours via Interstate 5 (typical)]
-
A.
travelTimeCategory
Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
-
B.
approximateDrivingTime
chosen
Indicates the estimated amount of time it takes to drive from one location to another under typical conditions.
-
C.
previousTravelTimeOnRoute
Indicates the duration of travel that occurred earlier on the same route before the current segment or time period.
-
D.
droveFor
Indicates that one entity operated a vehicle on behalf of, or in service to, another entity for a certain period or purpose.
-
E.
distanceTraveled
Indicates the total length of the path an entity has moved over a period of time or between two points.
- 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_69e0b4b3a6e08190ae663701f50fab8e |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a06b923c81908ba24be6645a7c61 |
completed | April 20, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69e59fdb7ad88190924176c32a195db3 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:37 a.m.