Triple
T1901887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Cliffs of Dover |
E37706
|
entity |
| Predicate | distanceToFrance |
P33783
|
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: [White Cliffs of Dover, distanceToFrance, about 34 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToFrance Context triple: [White Cliffs of Dover, distanceToFrance, about 34 kilometers]
-
A.
distanceFromParisCenter
Indicates the measured distance between a given location and the central point of Paris.
-
B.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
C.
distanceToLondon
Indicates the measured distance between a given entity’s location and the city of London.
-
D.
distanceToGeneva
Indicates the spatial distance between a given entity and the location of Geneva.
-
E.
distanceToMadrid
Indicates the physical distance between a given location or entity and the city of Madrid.
- 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_69a8861be7148190a680937ec451a304 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb34d94fc8190a5bf1e582c77c725 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafe9f8b0819086d8f6288511c66d |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb34c4a64819096e12b152b84c334 |
completed | March 7, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:35 p.m.