Triple
T7487717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aubagne |
E176922
|
entity |
| Predicate | distanceToMarseilleKilometers |
P76525
|
FINISHED |
| Object | about 17 |
—
|
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 17 | Statement: [Aubagne, distanceToMarseilleKilometers, about 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMarseilleKilometers Context triple: [Aubagne, distanceToMarseilleKilometers, about 17]
-
A.
distanceToMontpellierKm
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Montpellier.
-
B.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
-
C.
distanceToMetzKilometers
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Metz.
-
D.
distanceFromFoixKilometres
Indicates the physical distance, measured in kilometers, between a given place or entity and the location of Foix.
-
E.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
- 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_69c69f24ac508190bb98fe927c0bd065 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f55965ac81909d3c3a5422b22d44 |
completed | March 27, 2026, 9:23 p.m. |
| PD | Predicate disambiguation | batch_69c6f03eeaa88190a5215772ed05ee9f |
completed | March 27, 2026, 9:01 p.m. |
| PDg | Predicate description generation | batch_69c6f105e320819091db3cdb1f1351f0 |
completed | March 27, 2026, 9:05 p.m. |
Created at: March 27, 2026, 3:43 p.m.