Triple
T23323156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Musselburgh Old Course |
E591211
|
entity |
| Predicate | townProximity |
P36605
|
FINISHED |
| Object | near Edinburgh |
—
|
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: near Edinburgh | Statement: [Musselburgh Old Course, townProximity, near Edinburgh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: townProximity Context triple: [Musselburgh Old Course, townProximity, near Edinburgh]
-
A.
nearbyUrbanCenter
chosen
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
B.
nearestTownDistance
Indicates the distance from a given location to the closest town.
-
C.
nearestCityTo
Indicates that one city is the closest in distance to a given location or entity compared to all other cities.
-
D.
nearbySettlementUS
Indicates that one settlement is geographically close to another settlement within the United States.
-
E.
featuresRegionalProximity
Indicates that one entity is located near or in close geographic proximity to a particular region or another entity.
- 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_69e25d1effe4819096907f95f610dbff |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1978731b0819090f92ef768f2a749 |
completed | April 29, 2026, 5:30 a.m. |
| PD | Predicate disambiguation | batch_69effcf8ca2c8190887d4f4656617d21 |
completed | April 28, 2026, 12:19 a.m. |
Created at: April 17, 2026, 5:07 p.m.