Triple
T24022590
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darren Star Productions |
E594864
|
entity |
| Predicate | typicalSettingOfWorks |
P33122
|
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: [Darren Star Productions, typicalSettingOfWorks, New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSettingOfWorks Context triple: [Darren Star Productions, typicalSettingOfWorks, New York City]
-
A.
genreOfWorkSetting
Indicates the genre category that characterizes the setting in which a work takes place.
-
B.
narrativeSettingOfWork
Indicates that a particular place, time, or context serves as the narrative setting in which a work’s story or events occur.
-
C.
settingOfWorks
chosen
Indicates that a place or environment serves as the primary setting where the events or narratives of one or more works take place.
-
D.
literaryGenreOfWorkAppearedIn
Indicates the literary genre of the work in which a given entity (such as a text, character, or element) appears.
-
E.
typicalStyleInFiction
Indicates the characteristic narrative or artistic style that an entity most commonly exhibits within fictional works.
- 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_69e288be2c288190a3a46006945557f7 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d7667ff08190bfd14aa4eb776f21 |
completed | April 29, 2026, 10:03 a.m. |
| PD | Predicate disambiguation | batch_69f17639d23c8190bed93434e2f9230a |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 9:52 p.m.