Triple
T38676881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jill Casey |
E943771
|
entity |
| Predicate | seriesPrimarySetting |
P14002
|
FINISHED |
| Object | Hamptons, New York |
—
|
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: Hamptons, New York | Statement: [Jill Casey, seriesPrimarySetting, Hamptons, New York]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: seriesPrimarySetting Context triple: [Jill Casey, seriesPrimarySetting, Hamptons, New York]
-
A.
primarySettingOf
Indicates that a location or context serves as the main or principal setting in which an entity (such as a story, event, or activity) takes place.
-
B.
primarySetting
chosen
Indicates that one entity serves as the main or central location, context, or environment in which the other entity’s events or activities primarily take place.
-
C.
primarySettingFeature
Indicates that a particular feature is the main or defining characteristic of a setting.
-
D.
settingOfPrimaryStories
Indicates the primary location or environment in which the main stories or narratives about an entity take place.
-
E.
primarySeries
Indicates that one entity is the main or principal sequence, set, or collection to which another entity is related or belongs.
- 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_69f76eec28708190b9c82a505fc278e0 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fdd92396788190ae1424bc1ae55844 |
completed | May 8, 2026, 12:37 p.m. |
| PD | Predicate disambiguation | batch_69fdd678f40481909a717a2daec83b36 |
completed | May 8, 2026, 12:26 p.m. |
Created at: May 3, 2026, 4:33 p.m.