Triple
T27782659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cafe 80's |
E699374
|
entity |
| Predicate | fictionalTownContext |
P179019
|
FINISHED |
| Object | Hill Valley, California |
—
|
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: Hill Valley, California | Statement: [Cafe 80's, fictionalTownContext, Hill Valley, California]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalTownContext Context triple: [Cafe 80's, fictionalTownContext, Hill Valley, California]
-
A.
fictionalCityContext
Indicates that the relationship or information is situated within, or pertains specifically to, the setting of a fictional city.
-
B.
fictionalTownName
Indicates that the entity is associated with the name of a town that exists only in fiction rather than in the real world.
-
C.
fictionalTownFeatured
Indicates that a fictional town is prominently depicted or serves as a key setting within a work or medium.
-
D.
fictionalStreetContext
Indicates that the relationship or action occurs within a fictional street setting or is contextualized by events, conditions, or features specific to an imagined street.
-
E.
fictionalCitySetting
Indicates that a narrative, event, or work is set in a city that is imaginary or does not exist in the real world.
- 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_69ef6a4b5a9081909c9111396c2be3d2 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f719cc31ec819099bebcf833b14d76 |
completed | May 3, 2026, 9:47 a.m. |
| PD | Predicate disambiguation | batch_69f71820c6c88190ab38b4fa626d22cc |
completed | May 3, 2026, 9:40 a.m. |
| PDg | Predicate description generation | batch_69f71995853c8190912025c0e83640c8 |
completed | May 3, 2026, 9:47 a.m. |
Created at: April 27, 2026, 5:11 p.m.