Triple
T10535445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queens Museum Panorama of the City of New York |
E248550
|
entity |
| Predicate | numberOfBuildingsDepicted |
P11484
|
FINISHED |
| Object | over 800,000 |
—
|
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: over 800,000 | Statement: [Queens Museum Panorama of the City of New York, numberOfBuildingsDepicted, over 800,000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBuildingsDepicted Context triple: [Queens Museum Panorama of the City of New York, numberOfBuildingsDepicted, over 800,000]
-
A.
numberOfBuildings
chosen
Indicates the total count of buildings associated with a given entity or within a specified context.
-
B.
towerCount
Indicates the number of towers associated with or present in a given entity or context.
-
C.
eraOfManyBuildings
Indicates a time period during which many buildings were constructed or existed.
-
D.
hasMultipleBuildings
Indicates that an entity possesses, controls, or is associated with more than one distinct building.
-
E.
numberOfTowers
Indicates the quantity of towers associated with or contained by a given 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d50a1b20d48190a51358ec1af80371 |
completed | April 7, 2026, 1:43 p.m. |
| PD | Predicate disambiguation | batch_69d4fb9729288190a0149f127acd7ae3 |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:31 p.m.