Triple
T36435342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhu Fangyu |
E897561
|
entity |
| Predicate | leagueTitlesWonWithTeam |
P111762
|
FINISHED |
| Object | Guangdong Southern Tigers |
—
|
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: Guangdong Southern Tigers | Statement: [Zhu Fangyu, leagueTitlesWonWithTeam, Guangdong Southern Tigers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leagueTitlesWonWithTeam Context triple: [Zhu Fangyu, leagueTitlesWonWithTeam, Guangdong Southern Tigers]
-
A.
teamTitlesWonWith
Indicates the number or specific titles a team has won in association with a particular entity (such as a player, coach, or organization).
-
B.
leagueTitleWonWithClub
chosen
Indicates that a person has won a league title while playing for or being associated with a specific club.
-
C.
team2LeagueTitlesContext
Indicates that the second team has won league titles within a specified contextual scope (such as a particular time period, competition, or condition).
-
D.
teamWithTitles
Indicates a relationship where a team is associated with one or more titles it has earned or holds.
-
E.
team2LeagueTitles
Indicates that a given team has won a specified number of league titles.
- 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_69f76e56636481908eda808ab0273401 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:10 p.m.