Triple
T27767552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karen Davis |
E701643
|
entity |
| Predicate | workLocationInStory |
P1527
|
FINISHED |
| Object | Tokyo, Japan |
—
|
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: Tokyo, Japan | Statement: [Karen Davis, workLocationInStory, Tokyo, Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workLocationInStory Context triple: [Karen Davis, workLocationInStory, Tokyo, Japan]
-
A.
locationInWork
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).
-
B.
depictsWorkLocation
Indicates that one entity visually represents the place where another entity performs its work or professional activities.
-
C.
locationOfWork
chosen
Indicates the place or site where an entity performs its work or carries out its professional activities.
-
D.
homeLocationInStory
Indicates the place that serves as a character’s primary home or base of residence within the context of the story.
-
E.
placeInWork
Indicates that one entity is located or occurs within the spatial or structural context of another entity in a work.
- 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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69f63894e5848190aec428392562ab06 |
completed | May 2, 2026, 5:47 p.m. |
| PD | Predicate disambiguation | batch_69f6370c8c7c8190a02ea82847bb6e76 |
completed | May 2, 2026, 5:40 p.m. |
Created at: April 27, 2026, 4:32 p.m.