Triple
T24848399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capucine |
E621817
|
entity |
| Predicate | modeledInCity |
P157409
|
FINISHED |
| Object | New York City |
—
|
NE NERFINISHED |
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: New York City | Statement: [Capucine, modeledInCity, New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modeledInCity Context triple: [Capucine, modeledInCity, New York City]
-
A.
modeledInCity
chosen
Indicates that an entity (such as a model or design) was created, developed, or constructed within a particular city.
-
B.
cityDepicted
Indicates that a work (such as an image, artwork, or document) visually or representationally depicts a particular city.
-
C.
basedInCity
Indicates that an entity has its primary location, headquarters, or main operations situated in a specified city.
-
D.
representedCity
Indicates that an entity serves as the official representative or proxy for a particular city.
-
E.
setInCityModelledOn
Indicates that a fictional or constructed city is based on, inspired by, or patterned after a real-world city.
- 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_69e2fac297e481909d3aedc75f585e42 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f44a417a58819081777e18dda149fd |
completed | May 1, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69f442b8479c8190a7c8e416ac9e28a0 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 5:20 a.m.