Triple
T35066615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arcadia Oaks Canal |
E1011747
|
entity |
| Predicate | townOfFictionalSetting |
P182071
|
FINISHED |
| Object | Arcadia Oaks |
—
|
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: Arcadia Oaks | Statement: [Arcadia Oaks Canal, townOfFictionalSetting, Arcadia Oaks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: townOfFictionalSetting Context triple: [Arcadia Oaks Canal, townOfFictionalSetting, Arcadia Oaks]
-
A.
cityOfFictionalLocation
Indicates that a fictional location is situated within or associated with a particular city.
-
B.
cityOfFictionalResidence
Indicates that a fictional character or entity resides in, or is associated with living in, a particular city within a narrative or fictional context.
-
C.
cityOfFictionalActivity
Indicates that a fictional activity, event, or storyline takes place in the specified city.
-
D.
cityOfFictionalOrigin
Indicates the city from which a fictional character, entity, or work is described as originating within its narrative or fictional universe.
-
E.
basedInFictionalLocation
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
- 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_69f76dd193108190af2528186f25b72a |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7865578d48190bf90e470634fd97d |
completed | May 3, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69f7841812f081909d878955d114088e |
completed | May 3, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69f78575917481909a3defd6a4c366bd |
completed | May 3, 2026, 5:27 p.m. |
Created at: May 3, 2026, 4:01 p.m.