Triple
T12627936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English National Opera |
E301563
|
entity |
| Predicate | hasTypeOfBuildingUsed |
P1267
|
FINISHED |
| Object | opera house |
—
|
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: opera house | Statement: [English National Opera, hasTypeOfBuildingUsed, opera house]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfBuildingUsed Context triple: [English National Opera, hasTypeOfBuildingUsed, opera house]
-
A.
containsBuildingType
Indicates that a location or area includes at least one building of the specified type.
-
B.
hasBuildingFrom
Indicates a relationship where a location or site possesses or includes a building that originates from, or was constructed in, a specified time period, source, or context.
-
C.
containsBuilding
Indicates that one location or area includes a building within its boundaries.
-
D.
hasBuildingComponent
Indicates that one entity includes, contains, or is composed of another entity as a physical building component.
-
E.
usesBuilding
chosen
Indicates that one entity makes use of, occupies, or operates within a particular building.
- 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_69d7bdeaf49c8190b13800111fa77ea3 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617b07ec8190b714f04ae6654060 |
completed | April 10, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69d960b195108190ac25bd95e644ace4 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:15 p.m.