Triple
T46504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HOME Manchester |
E911
|
entity |
| Predicate | hasNumberOfTheatres |
P2427
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [HOME Manchester, hasNumberOfTheatres, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfTheatres Context triple: [HOME Manchester, hasNumberOfTheatres, 2]
-
A.
hasNumberOfPlatforms
Indicates the relationship that specifies how many platforms are associated with a given entity.
-
B.
theaterCommander
Indicates that an entity serves as the commanding authority over military operations within a specific theater or area of operations.
-
C.
theater
Indicates that an entity is a theater or is functioning in the role of a theater (a venue where performances or films are shown).
-
D.
hasNumberOfHouses
Indicates the quantity of houses associated with a given entity.
-
E.
listedOn
Indicates that an item, entity, or piece of information appears as an entry on a particular list, platform, or catalog.
- F. None of above. chosen
Provenance (4 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24b1bf2c081908f20e13939b713ff |
completed | Feb. 28, 2026, 1:55 a.m. |
| PD | Predicate disambiguation | batch_69a24abd07508190a83ffba5368c1c79 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24b1a42888190b56a5e457e11604f |
completed | Feb. 28, 2026, 1:55 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.