Triple
T29106848
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weiqi |
E736783
|
entity |
| Predicate | hasProfessionalSceneIn |
P84945
|
FINISHED |
| Object | 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: China | Statement: [Weiqi, hasProfessionalSceneIn, China]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionalSceneIn Context triple: [Weiqi, hasProfessionalSceneIn, China]
-
A.
hasRomanticSceneAt
Indicates that a romantic scene occurs at a specific location or point in time within a work or context.
-
B.
hasWeddingSceneWith
Indicates that two entities appear together in a wedding scene within the same context or work.
-
C.
hasFightSceneWith
Indicates that two entities participate together in a fight scene or combat sequence.
-
D.
hasRegionalScene
chosen
Indicates that something possesses or is associated with a specific regional scene, such as a localized cultural, artistic, or social milieu.
-
E.
hasCrimeScene
Indicates that a particular location or setting is the site where a specific crime occurred or was discovered.
- 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_69f077ec765c81909474c88bcc8bab43 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69ff3e1762d8819089a60e402e682817 |
completed | May 9, 2026, 2 p.m. |
| PD | Predicate disambiguation | batch_69ff3d8c6f308190a0646b1432752eb8 |
completed | May 9, 2026, 1:58 p.m. |
Created at: April 28, 2026, 11:16 a.m.