Triple
T1743410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Giverny cemetery |
E38281
|
entity |
| Predicate | distanceToParis |
P10703
|
FINISHED |
| Object | approximately 75 km west |
—
|
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: approximately 75 km west | Statement: [Giverny cemetery, distanceToParis, approximately 75 km west]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToParis Context triple: [Giverny cemetery, distanceToParis, approximately 75 km west]
-
A.
distanceFromParisCenter
chosen
Indicates the measured distance between a given location and the central point of Paris.
-
B.
distanceToLondon
Indicates the measured distance between a given entity’s location and the city of London.
-
C.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
D.
distanceToNewYorkCity
Indicates the spatial distance between a given entity’s location and New York City.
-
E.
distanceToMadrid
Indicates the physical distance between a given location or entity and the city of Madrid.
- 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab630e7d008190a8c673665d9672bb |
completed | March 6, 2026, 11:28 p.m. |
| PD | Predicate disambiguation | batch_69aa61c5a18481909bc49e0c54d64314 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.