Triple
T12696236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moganshan |
E303340
|
entity |
| Predicate | distanceFromHangzhou |
P76782
|
FINISHED |
| Object | approximately 60 km |
—
|
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 km | Statement: [Moganshan, distanceFromHangzhou, approximately 60 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromHangzhou Context triple: [Moganshan, distanceFromHangzhou, approximately 60 km]
-
A.
distanceToHangzhou
chosen
Indicates the physical distance between a given location and the city of Hangzhou.
-
B.
distanceToSuzhou
Indicates the measured spatial distance between a given entity and the location Suzhou.
-
C.
distanceFromBeijing_km
Indicates the physical distance, measured in kilometers, between a given place or object and Beijing.
-
D.
distanceToShanghai
Indicates the measured or specified distance between a given entity’s location and the city of Shanghai.
-
E.
distanceFromChengdu
Indicates the measured spatial distance between a given location and the city of Chengdu.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d962a32c6481908ddaddae4ea267bf |
completed | April 10, 2026, 8:50 p.m. |
| PD | Predicate disambiguation | batch_69d960be63f081908a5ef5ef17a311bf |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:22 p.m.