Triple
T23407772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phoenix Snow Park |
E559981
|
entity |
| Predicate | distanceFromPyeongchangOlympicPlaza |
P152146
|
FINISHED |
| Object | approximately 30 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: approximately 30 kilometers | Statement: [Phoenix Snow Park, distanceFromPyeongchangOlympicPlaza, approximately 30 kilometers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromPyeongchangOlympicPlaza Context triple: [Phoenix Snow Park, distanceFromPyeongchangOlympicPlaza, approximately 30 kilometers]
-
A.
distanceFromPyongyang
Indicates the measured spatial distance between a given location and the city of Pyongyang.
-
B.
distanceFromGwangju
Indicates the spatial distance between a given location and the city of Gwangju.
-
C.
distanceToSeoul
Indicates the measured or estimated spatial distance between a given entity’s location and the city of Seoul.
-
D.
distanceFromBeijingCityCenter
Indicates the physical distance between an entity’s location and the geographic center of Beijing city.
-
E.
distanceFromBeijing_km
Indicates the physical distance, measured in kilometers, between a given place or object and Beijing.
- 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_69e2454b3a5881909c64773dc8a5d289 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a50f3f90819084fb682597fee1e1 |
completed | April 29, 2026, 6:28 a.m. |
| PD | Predicate disambiguation | batch_69f061ed34288190a2e5e8cae03b0095 |
completed | April 28, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f07cbbd7488190ab3c8ae7d0fb68bf |
completed | April 28, 2026, 9:24 a.m. |
Created at: April 17, 2026, 5:38 p.m.