Triple
T21378111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Draveil |
E527265
|
entity |
| Predicate | distanceToParisCenterKilometersApprox |
P10703
|
FINISHED |
| Object | 20 |
—
|
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: 20 | Statement: [Draveil, distanceToParisCenterKilometersApprox, 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToParisCenterKilometersApprox Context triple: [Draveil, distanceToParisCenterKilometersApprox, 20]
-
A.
distanceFromParisCenter
chosen
Indicates the measured distance between a given location and the central point of Paris.
-
B.
distanceFromParisSaintLazare
Indicates the physical distance between a given place and Paris Saint-Lazare railway station.
-
C.
distanceToBordeauxCenter
Indicates the measured or calculated distance between a given entity’s location and the center of Bordeaux.
-
D.
distanceFromParisGareDeLyon
Indicates the distance between an entity and Paris Gare de Lyon railway station.
-
E.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
- 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0ca5e1c81909b06958459c553dc |
completed | April 22, 2026, 11:28 a.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:11 p.m.