Triple
T22966070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ziyang |
E571048
|
entity |
| Predicate | distanceToChengduTianfuInternationalAirport |
P79745
|
FINISHED |
| Object | relatively close |
—
|
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: relatively close | Statement: [Ziyang, distanceToChengduTianfuInternationalAirport, relatively close]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToChengduTianfuInternationalAirport Context triple: [Ziyang, distanceToChengduTianfuInternationalAirport, relatively close]
-
A.
distanceFromChengdu
Indicates the measured spatial distance between a given location and the city of Chengdu.
-
B.
distanceToTurin
Indicates the spatial distance between a given entity or location and the city of Turin.
-
C.
distanceToAirport
chosen
Indicates the measured distance between a given location and the nearest or specified airport.
-
D.
distanceFromTurin
Indicates the spatial distance separating an entity from the location of Turin.
-
E.
distanceFromMilan
Indicates the spatial distance between a given entity and the city of Milan.
- 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_69e245b2c6548190a0e4c7f2f7df2d48 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1822e542c8190a865f18e64fc0768 |
completed | April 29, 2026, 3:59 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9101f48190a06c69dff26c6441 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:47 p.m.