Triple
T410083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Million Dollar Staircase |
E9468
|
entity |
| Predicate | interiorFeatureOf |
P6655
|
FINISHED |
| Object | seat of the New York State Legislature |
—
|
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: seat of the New York State Legislature | Statement: [Million Dollar Staircase, interiorFeatureOf, seat of the New York State Legislature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: interiorFeatureOf Context triple: [Million Dollar Staircase, interiorFeatureOf, seat of the New York State Legislature]
-
A.
hasInteriorFeature
chosen
Indicates that an entity contains or includes a specific feature within its interior space.
-
B.
interiorStyle
Indicates that one entity has a particular interior design style or aesthetic characterized by the other entity.
-
C.
furnishingType
Indicates the type or category of furnishings associated with an entity, such as a property or room.
-
D.
chamberType
Indicates the specific kind or category of chamber associated with an entity (e.g., room, compartment, or enclosed space type).
-
E.
cabinet
Indicates that one entity serves as a cabinet (a storage or enclosure unit) for another entity.
- 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ed31681c8190ac32334562fb17fd |
completed | Feb. 28, 2026, 1:27 p.m. |
| PD | Predicate disambiguation | batch_69a2e9737694819080fde9adcc1aa4d4 |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.