Triple
T2380220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Hangover Part III |
E46293
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Leslie Chow |
E232088
|
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: Leslie Chow | Statement: [The Hangover Part III, character, Leslie Chow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leslie Chow Context triple: [The Hangover Part III, character, Leslie Chow]
-
A.
Leslie Chow
chosen
Leslie Chow is a wildly eccentric, foul-mouthed, and unpredictable gangster character from The Hangover film series, known for his outrageous antics and chaotic energy.
-
B.
Kelly Chan
Kelly Chan is a Hong Kong Cantopop singer and actress known for her popularity in the late 1990s and 2000s.
-
C.
Vivian Chan
Vivian Chan is a personal name shared by multiple individuals, including professionals in fields such as science, media, and business.
-
D.
Lai-Sang Young
Lai-Sang Young is a prominent mathematician known for her influential work in dynamical systems and ergodic theory.
-
E.
Anita Chan
Anita Chan is a prominent scholar known for her influential research on Chinese labor issues and labor rights.
- 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_69a88a1554a48190a0180682bcf099be |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abc7b60c8c819080e4f682e4362a93 |
completed | March 7, 2026, 6:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aebf36dcac8190a17d9af8cd1660c4 |
completed | March 9, 2026, 12:38 p.m. |
Created at: March 4, 2026, 7:57 p.m.