Triple
T27266969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seillans |
E687929
|
entity |
| Predicate | distanceToCannesKilometersApprox |
P45415
|
FINISHED |
| Object | 40 |
—
|
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: 40 | Statement: [Seillans, distanceToCannesKilometersApprox, 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToCannesKilometersApprox Context triple: [Seillans, distanceToCannesKilometersApprox, 40]
-
A.
distanceFromCannes
chosen
Indicates the spatial distance separating a given entity from the location of Cannes.
-
B.
distanceToMarseilleKilometers
Indicates the physical distance, measured in kilometers, between a given location or entity and the city of Marseille.
-
C.
distanceFromNice
Indicates the spatial distance between an entity and the location referred to as Nice.
-
D.
distanceToToulon_km
Indicates the distance, measured in kilometers, between a given entity’s location and the city of Toulon.
-
E.
distanceFromNiceByRoad_km
Indicates the length of the road route, in kilometers, from the city of Nice to the given location.
- 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_69ef3557abc481908bf3c146f0f3356a |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69ff1e3e13c08190bb8990c44716b746 |
completed | May 9, 2026, 11:45 a.m. |
| PD | Predicate disambiguation | batch_69ff1dfcaf2c8190aaf2b428d57b7782 |
completed | May 9, 2026, 11:43 a.m. |
Created at: April 27, 2026, 10:56 a.m.