Triple
T11391180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Talloires |
E269837
|
entity |
| Predicate | distanceToAnnecy |
P86945
|
FINISHED |
| Object | about 13 kilometres |
—
|
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 13 kilometres | Statement: [Talloires, distanceToAnnecy, about 13 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToAnnecy Context triple: [Talloires, distanceToAnnecy, about 13 kilometres]
-
A.
distanceToAnnecyKilometresApprox
chosen
Indicates an approximate distance, measured in kilometers, between a given entity or location and Annecy.
-
B.
distanceToGrenobleKilometers
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Grenoble.
-
C.
distanceToLePuy-en-Velay
Indicates the spatial distance between a given entity and the location of Le Puy-en-Velay.
-
D.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
-
E.
distanceToJuraMountains
Indicates the spatial distance between a given entity and the Jura Mountains.
- 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d800160a1c81909d115bf89fe54a49 |
completed | April 9, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69d7e70b228c8190b87f5101fd683788 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:34 p.m.