Triple
T4911227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Her Majesty's Theatre, London |
E110234
|
entity |
| Predicate | hasStalls |
P59386
|
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: [Her Majesty's Theatre, London, hasStalls, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStalls Context triple: [Her Majesty's Theatre, London, hasStalls, true]
-
A.
hasChoirStalls
Indicates that a location or structure contains or is equipped with choir stalls used for seating members of a choir.
-
B.
stallMarkedBy
Indicates that a stall or booth is identified or distinguished by a particular mark, label, or sign.
-
C.
hasStand
Indicates that an entity possesses, is equipped with, or is supported by a stand or base structure.
-
D.
hasStabling
Indicates that one entity provides or contains stabling facilities (such as stalls or accommodation) for another, typically for animals like horses.
-
E.
numberOfStalls
Indicates the quantity of stalls associated with or contained within a given entity or location.
- 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_69bd44132b94819088522d92beaadc78 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e9acc0c819080b07e2e7924d163 |
completed | March 20, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69bd6c325e188190823836d79934e9bc |
completed | March 20, 2026, 3:48 p.m. |
| PDg | Predicate description generation | batch_69bd6cc228088190849f23b7bd4cf549 |
completed | March 20, 2026, 3:50 p.m. |
Created at: March 20, 2026, 1:29 p.m.