Triple
T11383409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Park Avenue office towers |
E269652
|
entity |
| Predicate | servedAsModelFor |
P17880
|
FINISHED |
| Object | later corporate skyscrapers in global cities |
—
|
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: later corporate skyscrapers in global cities | Statement: [Park Avenue office towers, servedAsModelFor, later corporate skyscrapers in global cities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servedAsModelFor Context triple: [Park Avenue office towers, servedAsModelFor, later corporate skyscrapers in global cities]
-
A.
modeledBy
Indicates that one entity serves as a model or representation of another, typically capturing its structure, behavior, or properties.
-
B.
hasModelledFor
chosen
Indicates that one entity has served as a model for another entity, typically in a professional or representational context such as art, photography, or fashion.
-
C.
introducedAsModel
Indicates that one entity is presented or identified to others in the role or capacity of a model.
-
D.
wasUsedBy
Indicates that something served as a tool, resource, or means for an agent to perform an action or achieve a result.
-
E.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d800160a1c81909d115bf89fe54a49 |
completed | April 9, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69d7e70b228c8190b87f5101fd683788 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:34 p.m.