Triple
T19897813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hyères |
E478196
|
entity |
| Predicate | distanceToToulon_km |
P137748
|
FINISHED |
| Object | approximately 16 |
—
|
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 16 | Statement: [Hyères, distanceToToulon_km, approximately 16]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToToulon_km Context triple: [Hyères, distanceToToulon_km, approximately 16]
-
A.
distanceToMarseilleKilometers
Indicates the physical distance, measured in kilometers, between a given location or entity and the city of Marseille.
-
B.
distanceFromToulouse
Indicates the measured spatial distance between a given entity and the location of Toulouse.
-
C.
distanceFromCalais
Indicates the measured distance separating a given place or object from the location of Calais.
-
D.
distanceFromNiceByRoad_km
Indicates the length of the road route, in kilometers, from the city of Nice to the given location.
-
E.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
- 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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6593ef8dc8190be5b64988eceafac |
completed | April 20, 2026, 4:50 p.m. |
| PD | Predicate disambiguation | batch_69e537ecda248190895c96afb6243823 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c136b081909cab9394b958390a |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:52 p.m.