Triple
T32310212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doc McCoy |
E825477
|
entity |
| Predicate | narrativeSettingType |
P55822
|
FINISHED |
| Object | American crime milieu |
—
|
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: American crime milieu | Statement: [Doc McCoy, narrativeSettingType, American crime milieu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: narrativeSettingType Context triple: [Doc McCoy, narrativeSettingType, American crime milieu]
-
A.
narrativeLocationType
chosen
Indicates the type or role of a location within the structure or context of a narrative (e.g., setting, origin, destination).
-
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.
placeOfSetting
Indicates the location or environment where an event, scene, or situation takes place.
-
D.
workOfFictionSetting
Indicates that a work of fiction is set in, or primarily takes place within, a particular location, time, or environment.
-
E.
fictionalStreetSetting
Indicates that an entity is set on or associated with a street that exists only within a fictional or imaginary 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_69f3491213b88190a57094d8697a7455 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fb2e940d5c8190bceae77daf4ef512 |
completed | May 6, 2026, 12:05 p.m. |
| PD | Predicate disambiguation | batch_69f9fec70bd881909c658a3c5020318b |
completed | May 5, 2026, 2:29 p.m. |
Created at: May 1, 2026, 12:45 a.m.