Triple
T16004545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kris Wu |
E388180
|
entity |
| Predicate | startedSoloCareer |
P120731
|
FINISHED |
| Object | 2014 |
—
|
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: 2014 | Statement: [Kris Wu, startedSoloCareer, 2014]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startedSoloCareer Context triple: [Kris Wu, startedSoloCareer, 2014]
-
A.
startedCareerWith
Indicates that an entity began its professional career associated with, employed by, or active for another specified entity.
-
B.
launchedCareerOf
Indicates that one entity’s actions, support, or involvement initiated or significantly advanced another entity’s professional career.
-
C.
startedBusinessWith
Indicates that two or more entities jointly initiated or founded a business together.
-
D.
startedStandUpCareerIn
Indicates that an individual began their stand-up comedy career in a particular place or context.
-
E.
startedStandUpCareer
Indicates that an individual began their professional involvement in stand-up comedy at a particular time or event.
- F. None of above. chosen
Provenance (4 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142dc081c819082527e3fa8773460 |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e173af801c8190bfc0f602831bb594 |
completed | April 16, 2026, 11:41 p.m. |
Created at: April 10, 2026, 4:55 a.m.