Triple
T28376529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramone's Body Shop |
E718769
|
entity |
| Predicate | nearbyFictionalLocation |
P47231
|
FINISHED |
| Object | Flo's V8 Café |
—
|
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: Flo's V8 Café | Statement: [Ramone's Body Shop, nearbyFictionalLocation, Flo's V8 Café]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyFictionalLocation Context triple: [Ramone's Body Shop, nearbyFictionalLocation, Flo's V8 Café]
-
A.
locatedNearFiction
chosen
Indicates that one fictional entity or place is situated close to another within an imagined or narrative context.
-
B.
neighborhoodOfFictionalSetting
Indicates that one fictional setting is a neighborhood or local area within another fictional setting.
-
C.
hasFictionalNearbyTown
Indicates that an entity is associated with a fictional town located in its vicinity or surrounding area.
-
D.
hasNearbyFictionalFeature
Indicates that an entity is located close to a fictional or imaginary geographic or structural feature.
-
E.
fictionalLocationAssociatedWith
Indicates a relationship where a fictional entity (such as a character, event, or work) is connected to or set in a particular fictional location.
- 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_69eff6ee5afc8190bd7375a29f0cc6c6 |
completed | April 27, 2026, 11:53 p.m. |
| NER | Named-entity recognition | batch_69f7bbf906d8819099020e548dd56bc9 |
completed | May 3, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69f7b9a2dcf88190a7c9e109e41267be |
completed | May 3, 2026, 9:09 p.m. |
Created at: April 28, 2026, 1:03 a.m.