Triple
T10604120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McKenzie, Brackman, Chaney and Kuzak |
E275828
|
entity |
| Predicate | fictionalCityContext |
P94880
|
FINISHED |
| Object | downtown Los Angeles |
—
|
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: downtown Los Angeles | Statement: [McKenzie, Brackman, Chaney and Kuzak, fictionalCityContext, downtown Los Angeles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalCityContext Context triple: [McKenzie, Brackman, Chaney and Kuzak, fictionalCityContext, downtown Los Angeles]
-
A.
partOfFictionalCity
Indicates that one entity is a component, area, or subdivision within a larger fictional city.
-
B.
fictionalPlaceType
Indicates that a place is a fictional location and specifies what type or category of fictional place it is.
-
C.
fictionalTownName
Indicates that the entity is associated with the name of a town that exists only in fiction rather than in the real world.
-
D.
cityOfFictionalActivity
Indicates that a fictional activity, event, or storyline takes place in the specified city.
-
E.
fictionalTownFeatured
Indicates that a fictional town is prominently depicted or serves as a key setting within a work or medium.
- F. None of above. chosen
Provenance (4 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df4992248190b640d743ccf02c82 |
completed | April 8, 2026, 11:05 p.m. |
| PD | Predicate disambiguation | batch_69d6dd72c1288190adbb5e79e94c044a |
completed | April 8, 2026, 10:57 p.m. |
| PDg | Predicate description generation | batch_69d6df463ea8819091d6683e476b4f21 |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 7:32 p.m.