Triple
T14798089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wallasey Town Hall |
E347830
|
entity |
| Predicate | hasAssemblyHall |
P114096
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Wallasey Town Hall, hasAssemblyHall, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssemblyHall Context triple: [Wallasey Town Hall, hasAssemblyHall, true]
-
A.
hasAuditorium
Indicates that one entity possesses or includes an auditorium as part of its facilities.
-
B.
hasAssembly
Indicates that an entity is composed of, or includes, one or more component parts assembled into a whole.
-
C.
hasLectureHall
Indicates that an entity possesses, includes, or is associated with a lecture hall as part of its facilities or structure.
-
D.
hasHallFunction
chosen
Indicates that an entity serves as or includes a hall used for gatherings, events, or similar functions.
-
E.
hasStationHall
Indicates that one entity (typically a station) includes or is associated with a station hall area as part of its structure or facilities.
- 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_69d822ea8b7c819097dfadf3d45545e6 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69decd6131f08190885f1d27b4bfac7a |
completed | April 14, 2026, 11:27 p.m. |
| PD | Predicate disambiguation | batch_69de8c090d1081909b5a9bf437499d6c |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:31 a.m.