Triple
T36036616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farmington detective squad |
E1042419
|
entity |
| Predicate | setInFictionalArea |
P18263
|
FINISHED |
| Object | Farmington |
—
|
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: Farmington | Statement: [Farmington detective squad, setInFictionalArea, Farmington]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setInFictionalArea Context triple: [Farmington detective squad, setInFictionalArea, Farmington]
-
A.
setInFictionalLocation
chosen
Indicates that an event, story, or narrative takes place within a fictional or imagined location rather than a real-world setting.
-
B.
setInFictionalizedRegionOf
Indicates that an event or narrative is located within a region that is a fictionalized or altered version of a real-world place.
-
C.
setInFictionalOrRealLocation
Indicates that something (such as a story, event, or scene) takes place within a specified location, whether that location is real or fictional.
-
D.
hasFictionalSettingElement
Indicates that something includes or is associated with a specific element or component of a fictional setting.
-
E.
operatesInFictionalSetting
Indicates that an entity carries out its activities or functions within a fictional or imaginary setting rather than a real-world context.
- 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_69f76e2d7e8c8190bac4e90734566799 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7ad1bf27081909683fa99a367f556 |
completed | May 3, 2026, 8:16 p.m. |
| PD | Predicate disambiguation | batch_69f7ab75387c819091afc3c2128eb903 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 3, 2026, 4:07 p.m.