Triple
T2747913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AJ Auxerre |
E60913
|
entity |
| Predicate | owner |
P347
|
FINISHED |
| Object | James Zhou |
E296158
|
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: James Zhou | Statement: [AJ Auxerre, owner, James Zhou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: James Zhou Context triple: [AJ Auxerre, owner, James Zhou]
-
A.
James Zhou
chosen
James Zhou is a Chinese businessman best known as the owner and chairman of French football club AJ Auxerre.
-
B.
Daniel Zhang
Daniel Zhang is a Chinese business executive best known for leading Alibaba Group through a major period of global expansion and for creating the Singles’ Day shopping festival.
-
C.
Yao Chen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
D.
Langche Zeng
Langche Zeng is a political scientist and quantitative methodologist known for his collaborative work with Gary King on statistical methods in social science research.
-
E.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
- 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_69ab4b79846081909096725374d65ce9 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdb4ff7b08190b72edb6a2bc5fd19 |
completed | March 7, 2026, 8:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc03bd03881908747845aede53f83 |
completed | March 10, 2026, 6:54 a.m. |
Created at: March 6, 2026, 9:56 p.m.