Triple
T15765417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Centrair |
E382206
|
entity |
| Predicate | distanceFromNagoyaCityCenterKilometers |
P120246
|
FINISHED |
| Object | about 35 |
—
|
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 35 | Statement: [Centrair, distanceFromNagoyaCityCenterKilometers, about 35]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromNagoyaCityCenterKilometers Context triple: [Centrair, distanceFromNagoyaCityCenterKilometers, about 35]
-
A.
distanceFromŌmiyaStation_km
Indicates the distance, measured in kilometers, between a given place and Ōmiya Station.
-
B.
distanceFromKochi_km
Indicates the physical distance, measured in kilometers, between a given location and Kochi.
-
C.
distanceFromShibuyaTerminus_km
Indicates the distance, measured in kilometers, from the Shibuya terminus to the referenced location or entity.
-
D.
distanceFromNaha
Indicates the spatial distance between a given location and Naha.
-
E.
distanceFromKyotoStation
Indicates the spatial distance between a given location and Kyoto Station.
- 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_69d86da09a10819082fe9797b23e4664 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e050b8154881908afe5191e6424f15 |
completed | April 16, 2026, 3 a.m. |
| PD | Predicate disambiguation | batch_69e00531e7ac8190a4190cce4f7fab4c |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e03cc871d0819085c0fc54de7984ff |
completed | April 16, 2026, 1:35 a.m. |
Created at: April 10, 2026, 4:47 a.m.