Triple
T5466501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boulogne-sur-Mer |
E122723
|
entity |
| Predicate | distanceToDoverKilometres |
P33782
|
FINISHED |
| Object | about 50 |
—
|
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 50 | Statement: [Boulogne-sur-Mer, distanceToDoverKilometres, about 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToDoverKilometres Context triple: [Boulogne-sur-Mer, distanceToDoverKilometres, about 50]
-
A.
distanceFromDover
chosen
Indicates the measured distance between a given entity or location and the place named Dover.
-
B.
distanceToFolkestone
Indicates the spatial distance between a given location or entity and the town of Folkestone.
-
C.
distanceToSunderland_km
Indicates the physical distance, measured in kilometers, between a given place and Sunderland.
-
D.
distanceToHunstanton
Indicates the measured spatial distance between a given entity and the location Hunstanton.
-
E.
distanceToLerwickKilometers
Indicates the physical distance, measured in kilometers, between a given entity’s location and the town of Lerwick.
- 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_69bd4643f16081908d7f29e08096115a |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927c946c8190aef40679199fede3 |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a370a88190b5d17b8a5387138d |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:08 p.m.