Triple
T24438228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wang the water seller |
E616181
|
entity |
| Predicate | workSettingPlace |
P1527
|
FINISHED |
| Object | Szechwan, China |
—
|
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: Szechwan, China | Statement: [Wang the water seller, workSettingPlace, Szechwan, China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: workSettingPlace Context triple: [Wang the water seller, workSettingPlace, Szechwan, China]
-
A.
workSetting
Indicates the environment, context, or conditions in which the work or activity is carried out.
-
B.
locationOfWork
chosen
Indicates the place or site where an entity performs its work or carries out its professional activities.
-
C.
placeInWork
Indicates that one entity is located or occurs within the spatial or structural context of another entity in a work.
-
D.
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).
-
E.
residesInWork
Indicates that a person or entity lives or is based within the location, setting, or environment defined by a particular work (such as a book, film, or other creative piece).
- 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_69e2d7ec44b081909ccaf1f3bbec0641 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f297881fa08190b82bdc5ebeae96f7 |
completed | April 29, 2026, 11:43 p.m. |
| PD | Predicate disambiguation | batch_69f287d3237c819099559c00f83131d8 |
completed | April 29, 2026, 10:36 p.m. |
Created at: April 18, 2026, 2:16 a.m.