Triple
T15347508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benxi |
E366961
|
entity |
| Predicate | distanceFromShenyang |
P118210
|
FINISHED |
| Object | approximately 60 kilometers southeast |
—
|
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 60 kilometers southeast | Statement: [Benxi, distanceFromShenyang, approximately 60 kilometers southeast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromShenyang Context triple: [Benxi, distanceFromShenyang, approximately 60 kilometers southeast]
-
A.
distanceFromBeijing_km
Indicates the physical distance, measured in kilometers, between a given place or object and Beijing.
-
B.
distanceFromBeijingCityCenter
Indicates the physical distance between an entity’s location and the geographic center of Beijing city.
-
C.
distanceToShanghai
Indicates the measured or specified distance between a given entity’s location and the city of Shanghai.
-
D.
distanceFromSaintPetersburg
Indicates the spatial distance between a given entity and the city of Saint Petersburg.
-
E.
distanceToSapporo
Indicates the measured or calculated distance between a given entity and the location of Sapporo.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e1749bc8190a8b9cbcb27288a5b |
completed | April 16, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69deca991e5081908b0df3d1ee7d5338 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2e413481909d9180a8d78d2c17 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:17 a.m.