Triple
T11954259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Flour |
E284509
|
entity |
| Predicate | distanceFromClermont-Ferrand |
P78330
|
FINISHED |
| Object | approximately 90 km south |
—
|
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 km south | Statement: [Saint-Flour, distanceFromClermont-Ferrand, approximately 90 km south]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromClermont-Ferrand Context triple: [Saint-Flour, distanceFromClermont-Ferrand, approximately 90 km south]
-
A.
distanceToClermontFerrand_km
chosen
Indicates the physical distance, measured in kilometers, between a given place and Clermont-Ferrand.
-
B.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
-
C.
distanceToSaint-Étienne
Indicates the measured or specified distance between a given entity and the location Saint-Étienne.
-
D.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
-
E.
distanceFromToulouse
Indicates the measured spatial distance between a given entity and the location of Toulouse.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90365da288190a132703df563de23 |
completed | April 10, 2026, 2:04 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3e48e08190b2fee43af4f57323 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.