Triple
T4112107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mutianyu section |
E90199
|
entity |
| Predicate | approximateDistanceInKilometers |
P22795
|
FINISHED |
| Object | 70 |
—
|
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: 70 | Statement: [Mutianyu section, approximateDistanceInKilometers, 70]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateDistanceInKilometers Context triple: [Mutianyu section, approximateDistanceInKilometers, 70]
-
A.
approximateDistanceKm
chosen
Indicates the estimated distance between two entities measured in kilometers, typically with some degree of inaccuracy or approximation.
-
B.
approximateDistanceFrom
Indicates an estimated or rough measure of how far one entity is from another.
-
C.
hasApproximateDrivingDistanceFrom
Indicates that one entity is located at an estimated or approximate driving distance from another entity, typically measured along road routes rather than as a precise or exact value.
-
D.
approximateLengthInMeters
Indicates the estimated or roughly measured length of something expressed in meters.
-
E.
approximateLengthInMiles
Indicates the estimated distance or extent of something measured in miles.
- 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_69aed95c080881908125e30c5dcdc6f8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af03d7240c8190a64dcbc669772808 |
completed | March 9, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69af0183eb84819087d7184de28f5514 |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:41 p.m.