Triple
T34066768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris–Nantes |
E873647
|
entity |
| Predicate | approximateTravelTimeByTGV |
P181490
|
FINISHED |
| Object | about 2 hours |
—
|
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 2 hours | Statement: [Paris–Nantes, approximateTravelTimeByTGV, about 2 hours]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateTravelTimeByTGV Context triple: [Paris–Nantes, approximateTravelTimeByTGV, about 2 hours]
-
A.
travelTimeToParisByRER
Indicates the duration required to travel to Paris using the RER train network.
-
B.
distanceFromParisSaintLazare
Indicates the physical distance between a given place and Paris Saint-Lazare railway station.
-
C.
servedByTransilienLine
Indicates that a location or facility is connected to and receives service from a specific Transilien railway line.
-
D.
railTravelTimeRangeHours
chosen
Indicates the range of time, in hours, that a journey by rail between the related entities is expected to take.
-
E.
distanceFromParisGareDeLyon
Indicates the distance between an entity and Paris Gare de Lyon railway station.
- 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_69f349a4af208190afa14888f9c9fb9d |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff0491409c8190be40f633a58da0b1 |
completed | May 9, 2026, 9:55 a.m. |
| PD | Predicate disambiguation | batch_69ff040bb5cc81909534c7eee85d5e90 |
completed | May 9, 2026, 9:53 a.m. |
Created at: May 1, 2026, 1:52 a.m.