Triple
T15145434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Île d’If |
E361792
|
entity |
| Predicate | distanceFromMarseille |
P76525
|
FINISHED |
| Object | about 1.5 kilometers offshore |
—
|
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 1.5 kilometers offshore | Statement: [Île d’If, distanceFromMarseille, about 1.5 kilometers offshore]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromMarseille Context triple: [Île d’If, distanceFromMarseille, about 1.5 kilometers offshore]
-
A.
distanceToMarseilleKilometers
chosen
Indicates the physical distance, measured in kilometers, between a given location or entity and the city of Marseille.
-
B.
distanceFromLyon
Indicates the spatial distance between a given entity and the city of Lyon.
-
C.
distanceToMontpellierKm
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Montpellier.
-
D.
distanceFromCalais
Indicates the measured distance separating a given place or object from the location of Calais.
-
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_69d85a0759908190b8a051d2e2a1cbe6 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005c71b688190b2e8ccfdf4db9037 |
completed | April 15, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69deb9713fe881909dec2fd3f6c84b39 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:07 a.m.