Triple
T12348652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Graven & Mayger |
E294425
|
entity |
| Predicate | typeOfBuildingDesigned |
P91519
|
FINISHED |
| Object | cinema theaters |
—
|
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: cinema theaters | Statement: [Graven & Mayger, typeOfBuildingDesigned, cinema theaters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfBuildingDesigned Context triple: [Graven & Mayger, typeOfBuildingDesigned, cinema theaters]
-
A.
intendedBuildingType
chosen
Indicates the type of building that something is planned or designed to be.
-
B.
buildingType
Indicates the specific category or function that characterizes what kind of building something is.
-
C.
architectureType
Indicates the specific style or category of architecture that characterizes or defines an entity.
-
D.
typeOfEnterpriseBuilt
Indicates the specific kind or category of enterprise that has been constructed or established.
-
E.
designedFacilityType
Indicates the type or category of facility that something (such as a plan, system, or component) is specifically designed for.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f7ba17481908b03af7316b28d9b |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ecb5efc819086a3530282278bb1 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:53 p.m.