Triple
T11339682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fast & Furious 6 |
E268560
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Han Lue |
E268558
|
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: Han Lue | Statement: [Fast & Furious 6, character, Han Lue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Han Lue Context triple: [Fast & Furious 6, character, Han Lue]
-
A.
Han Lue
chosen
Han Lue is a laid-back, skilled street racer and heist crew member in the Fast & Furious franchise, known for his calm demeanor, drifting talent, and constant snacking.
-
B.
Shu Chien
Shu Chien is a renowned Chinese-American physiologist and bioengineer recognized for pioneering contributions to cardiovascular biomechanics and microcirculation research.
-
C.
Cui Hao
Cui Hao was a prominent poet of the Tang dynasty in China, best known for his evocative landscape and frontier poems.
-
D.
Li Chu
Li Chu, better known as Emperor Daizong of Tang, was a Chinese emperor who ruled during the mid-Tang dynasty and worked to restore stability after the An Lushan Rebellion.
-
E.
Tie Luo Han
Tie Luo Han is a famous and highly prized Wuyi rock oolong tea from China, known for its rich, roasted flavor and mineral complexity.
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea01c6c08190910a6ce8fb7e186d |
completed | April 9, 2026, 6:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58b7033448190b848ccd3712c0b0e |
completed | April 20, 2026, 2:12 a.m. |
Created at: April 8, 2026, 9:33 p.m.