Triple
T25917298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hollywood Arts High School |
E653067
|
entity |
| Predicate | setInFictionalVersionOf |
P107279
|
FINISHED |
| Object | Los Angeles |
—
|
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: Los Angeles | Statement: [Hollywood Arts High School, setInFictionalVersionOf, Los Angeles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setInFictionalVersionOf Context triple: [Hollywood Arts High School, setInFictionalVersionOf, Los Angeles]
-
A.
setInFictionalizedRegionOf
chosen
Indicates that an event or narrative is located within a region that is a fictionalized or altered version of a real-world place.
-
B.
setInFictionalLocation
Indicates that an event, story, or narrative takes place within a fictional or imagined location rather than a real-world setting.
-
C.
isSetInFictionalUniverse
Indicates that a narrative work takes place within a specific fictional universe or setting.
-
D.
worksInFictionalContext
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
-
E.
setInFictionalYear
Indicates that the events or narrative of a work are situated in a specified fictional or non-real calendar year.
- 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_69e7ab3e025c819086771607157f0015 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f603e5fc688190b5669020dafbb7c7 |
completed | May 2, 2026, 2:02 p.m. |
| PD | Predicate disambiguation | batch_69f4a10480748190a2e67bd399fc435d |
completed | May 1, 2026, 12:48 p.m. |
Created at: April 22, 2026, 8:31 a.m.