Triple
T23338536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sung-tae |
E591667
|
entity |
| Predicate | hasNameElement |
P3097
|
FINISHED |
| Object | Sung |
—
|
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: Sung | Statement: [Sung-tae, hasNameElement, Sung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sung Context triple: [Sung-tae, hasNameElement, Sung]
-
A.
Sung
chosen
Sung is the given name of actor Sung Kang, best known for his role as Han in the Fast & Furious film franchise.
-
B.
Song Sun
Song Sun is a prominent Chinese-American mathematician known for his influential work in complex differential geometry and geometric analysis, particularly on Kähler–Einstein metrics and Ricci flow.
-
C.
Sang
Sang is a French term meaning "blood," often used to denote a deep, vivid red color.
-
D.
Sinthee
Sinthee is a neighborhood in North Kolkata, India, known for its residential character and proximity to areas like Baranagar and Dum Dum.
-
E.
Sonning
Sonning is a picturesque village on the River Thames in Berkshire, England, known for its historic architecture, scenic riverside setting, and literary associations.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1983099188190a2e05cf81d62a641 |
completed | April 29, 2026, 5:33 a.m. |
Created at: April 17, 2026, 5:17 p.m.