Triple
T908172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinese Civil War (early phase) |
E19597
|
entity |
| Predicate | hasMainTheater |
P5783
|
FINISHED |
| Object | mainland China |
—
|
LITERAL FINISHED |
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: mainland China | Statement: [Chinese Civil War (early phase), hasMainTheater, mainland China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainTheater Context triple: [Chinese Civil War (early phase), hasMainTheater, mainland China]
-
A.
primaryTheater
chosen
Indicates that a particular location or region is the main setting or principal area where an event, activity, or operation takes place.
-
B.
appliesToTheater
Indicates that something is relevant or applicable specifically to a theater or theatrical context.
-
C.
hasAuditorium
Indicates that one entity possesses or includes an auditorium as part of its facilities.
-
D.
hasNumberOfTheatres
Indicates the quantity of theatres associated with or present in a given entity.
-
E.
servedInTheatres
Indicates that a film or performance was publicly exhibited in movie theaters or similar cinema venues.
- 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_69a4939e889c8190ac148b3ac1a7f90b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3bcad2481908b83575b2fb80d14 |
completed | March 1, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69a4b28ff5948190982c4439eadf9d87 |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:39 p.m.