Triple
T7080857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Perpignan |
E164948
|
entity |
| Predicate | distanceToBarcelonaKm |
P74868
|
FINISHED |
| Object | about 160 |
—
|
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 160 | Statement: [Perpignan, distanceToBarcelonaKm, about 160]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBarcelonaKm Context triple: [Perpignan, distanceToBarcelonaKm, about 160]
-
A.
distanceToMadrid
Indicates the physical distance between a given location or entity and the city of Madrid.
-
B.
distanceToPamplona
Indicates the spatial distance between a given entity and the location of Pamplona.
-
C.
distanceToIbiza
Indicates the spatial distance between a given entity’s location and the location of Ibiza.
-
D.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
E.
distanceToBasel_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Basel.
- 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_69c6887cbc6c8190bdfac42d940f4d8a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4f1f5748190b214856bcfc70d81 |
completed | March 27, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69c6e1bfcb948190a5ada74fb8c054cb |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e4a15b088190bee9a23e94aaac53 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:40 p.m.