Triple
T32938837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leigh parish |
E842610
|
entity |
| Predicate | encompassedTownship |
P22464
|
FINISHED |
| Object | Atherton |
—
|
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: Atherton | Statement: [Leigh parish, encompassedTownship, Atherton]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: encompassedTownship Context triple: [Leigh parish, encompassedTownship, Atherton]
-
A.
hasTownship
chosen
Indicates that one administrative area or jurisdiction includes or is associated with a specific township.
-
B.
roughlyEncompassed
Indicates that one entity includes or surrounds another in an approximate or imprecise manner, without strict or exact boundaries.
-
C.
neighboringTownship
Indicates that two townships share a common boundary and are directly adjacent to each other.
-
D.
regionContainsTowns
Indicates that a geographic region includes or encompasses one or more towns within its boundaries.
-
E.
involvedTown
Indicates that a town participates in, is associated with, or is affected by a particular event, activity, or relationship.
- 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_69f34949727c81909d195c97de3341c8 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d10ea8f481908d142fcf50112b9c |
completed | May 3, 2026, 4:37 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe5f93c8190995c53dbbe380a32 |
completed | May 3, 2026, 4:32 a.m. |
Created at: May 1, 2026, 1:20 a.m.