Triple
T35626144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cherry Bang |
E1029458
|
entity |
| Predicate | workLocationInUniverse |
P88991
|
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: [Cherry Bang, workLocationInUniverse, Los Angeles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workLocationInUniverse Context triple: [Cherry Bang, workLocationInUniverse, Los Angeles]
-
A.
builtLocationInUniverse
Indicates that a construction or structure was created or established at a specific location within a particular universe or cosmic setting.
-
B.
inUniverseLocationType
Indicates the type or category of location that something occupies within a fictional or defined universe or setting.
-
C.
hasInUniverseLocationRelation
Indicates a relationship where an entity is associated with, or situated at, a specific location within a fictional or defined universe or setting.
-
D.
employerInUniverse
Indicates that one entity serves as the employer of another within a specified universe, context, or world.
-
E.
locationInWork
chosen
Indicates that one entity specifies the place or setting where another entity occurs, is situated, or takes place within a particular work (e.g., a scene’s location in a film or a chapter’s setting in a book).
- 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_69f76e07bb0c8190968ea2d836fc42c9 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79f151c308190856dabf20ddafb02 |
completed | May 3, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69f79e4bdbcc8190be7a0d2cf8a77b64 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:05 p.m.