Triple
T23442649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | H. Jon Benjamin as Bob Belcher |
E565443
|
entity |
| Predicate | characterPrimarySetting |
P90820
|
FINISHED |
| Object | Bob's Burgers restaurant |
—
|
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: Bob's Burgers restaurant | Statement: [H. Jon Benjamin as Bob Belcher, characterPrimarySetting, Bob's Burgers restaurant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterPrimarySetting Context triple: [H. Jon Benjamin as Bob Belcher, characterPrimarySetting, Bob's Burgers restaurant]
-
A.
placeOfSetting
Indicates the location or environment where an event, scene, or situation takes place.
-
B.
settingOfFictionalResidence
Indicates that a location serves as the setting or backdrop for a fictional residence within a narrative work.
-
C.
characterSetting
chosen
Indicates that a character is associated with, appears in, or is situated within a particular setting or environment.
-
D.
fictionalStreetSetting
Indicates that an entity is set on or associated with a street that exists only within a fictional or imaginary context.
-
E.
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.
- 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64654e88190b530958b27b32412 |
completed | April 29, 2026, 6:33 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:51 p.m.