Triple
T15323062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wolverhampton Wanderers F.C. |
E366340
|
entity |
| Predicate | chairman |
P377
|
FINISHED |
| Object | Jeff Shi |
E366343
|
NE 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: Jeff Shi | Statement: [Wolverhampton Wanderers F.C., chairman, Jeff Shi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeff Shi Context triple: [Wolverhampton Wanderers F.C., chairman, Jeff Shi]
-
A.
Jeff Shi
chosen
Jeff Shi is a Chinese businessman best known for leading Wolverhampton Wanderers F.C. as its chairman under the ownership of Fosun International.
-
B.
Jia Deng
Jia Deng is a computer scientist known for his influential work in computer vision and machine learning, particularly as a co-creator of the large-scale image dataset ImageNet.
-
C.
Philip S. Yu
Philip S. Yu is a prominent computer scientist known for his influential contributions to data mining, databases, and big data analytics.
-
D.
Edward J. Pei
Edward J. Pei is a film cinematographer known for his work on feature films such as the biographical drama "Why Do Fools Fall in Love."
-
E.
Wei Liu
Wei Liu is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work on object detection.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dd5ce0c819093c9a14de549dff6 |
completed | April 16, 2026, 1:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef8aaef608190bd3ec9fdd215afbb |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:16 a.m.