Triple
T30776045
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Symington |
E783671
|
entity |
| Predicate | hasHistoricCountyTownNearby |
P13121
|
FINISHED |
| Object | Ayr |
—
|
NE NERFINISHED |
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: Ayr | Statement: [Symington, hasHistoricCountyTownNearby, Ayr]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHistoricCountyTownNearby Context triple: [Symington, hasHistoricCountyTownNearby, Ayr]
-
A.
hasHistoricCountyTownRelation
Indicates a relationship where a town serves or has served as the historic county town (traditional administrative center) of a given county.
-
B.
containsHistoricTown
Indicates that one entity geographically includes or encompasses a town that has recognized historical significance.
-
C.
hasHistoricCountySeat
chosen
Indicates that an administrative region historically had its county government or main county offices located in a particular settlement or city.
-
D.
hasAssociatedHistoricCounty
Indicates that an entity is linked to a specific historic county with which it is geographically or administratively associated.
-
E.
historicalTownshipOf
Indicates that one entity was formerly a township encompassing or governing the other entity during a past historical period.
- 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_69f224b1519081908b9db003fd2073e0 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fde5d7d9548190880a9d95b8f0f66b |
completed | May 8, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69fde4e1bf9c81909754545275eccc03 |
completed | May 8, 2026, 1:28 p.m. |
Created at: April 29, 2026, 8:40 p.m.