Triple
T10371924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valence, Drôme |
E244403
|
entity |
| Predicate | distanceToGrenobleKilometers |
P93638
|
FINISHED |
| Object | approximately 90 |
—
|
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 90 | Statement: [Valence, Drôme, distanceToGrenobleKilometers, approximately 90]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToGrenobleKilometers Context triple: [Valence, Drôme, distanceToGrenobleKilometers, approximately 90]
-
A.
distanceFromBesançonKilometres
Indicates the distance, measured in kilometers, between an entity and the city of Besançon.
-
B.
distanceToClermontFerrand_km
Indicates the physical distance, measured in kilometers, between a given place and Clermont-Ferrand.
-
C.
distanceToAnnecyKilometresApprox
Indicates an approximate distance, measured in kilometers, between a given entity or location and Annecy.
-
D.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
-
E.
distanceToJuraMountains
Indicates the spatial distance between a given entity and the Jura Mountains.
- 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e97ed09c8190a3627aa7b5eea62f |
completed | April 7, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69d4dface5508190a7b42f01ad0a19a2 |
completed | April 7, 2026, 10:42 a.m. |
| PDg | Predicate description generation | batch_69d4e944fac4819093b0312aa0efd729 |
completed | April 7, 2026, 11:23 a.m. |
Created at: April 6, 2026, 12:01 p.m.