Triple
T8910457
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cap Gris-Nez |
E212168
|
entity |
| Predicate | distanceToEnglandAtClosestPoint |
P73340
|
FINISHED |
| Object | about 34 kilometers |
—
|
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 34 kilometers | Statement: [Cap Gris-Nez, distanceToEnglandAtClosestPoint, about 34 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToEnglandAtClosestPoint Context triple: [Cap Gris-Nez, distanceToEnglandAtClosestPoint, about 34 kilometers]
-
A.
distanceToFolkestone
Indicates the spatial distance between a given location or entity and the town of Folkestone.
-
B.
distanceToLondon
Indicates the measured distance between a given entity’s location and the city of London.
-
C.
distanceToEnglishBorder
chosen
Indicates the spatial distance between a given location and the border of England.
-
D.
distanceToSaintHelena
Indicates the measured distance between a given entity and the location of Saint Helena.
-
E.
distanceToFrance
Indicates the spatial distance between a given entity and the country of France.
- 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_69ca839255248190b43984294abd92ae |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc65227d008190b13ba162d0b3c9d1 |
completed | April 1, 2026, 12:21 a.m. |
| PD | Predicate disambiguation | batch_69cc5ecf55248190a29f00fbf99f13c4 |
completed | March 31, 2026, 11:54 p.m. |
Created at: March 30, 2026, 6:55 p.m.