Triple
T11821843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Hatters |
E281156
|
entity |
| Predicate | reflectsTownAssociation |
P77764
|
FINISHED |
| Object | Stockport hat-making industry |
—
|
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: Stockport hat-making industry | Statement: [The Hatters, reflectsTownAssociation, Stockport hat-making industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reflectsTownAssociation Context triple: [The Hatters, reflectsTownAssociation, Stockport hat-making industry]
-
A.
fromTown
chosen
Indicates that one entity originates from, or is associated as being from, a particular town represented by the other entity.
-
B.
involvedTown
Indicates that a town participates in, is associated with, or is affected by a particular event, activity, or relationship.
-
C.
hasTown
Indicates that one entity possesses, contains, or is associated with a town as part of its structure, jurisdiction, or composition.
-
D.
hasTownship
Indicates that one administrative area or jurisdiction includes or is associated with a specific township.
-
E.
associatedWithTempleTown
Indicates a relationship where an entity has a connection or linkage to a town that is characterized by or centered around a temple.
- 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_69d6ab26aae88190b2489efcb2a24234 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a5e9c50481909b2287a0b23d2094 |
completed | April 10, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69d8a251fc08819095933f1d13c3b742 |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:42 p.m.