Triple
T20141180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Player 218 |
E491167
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Park Hae-soo |
—
|
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: Park Hae-soo | Statement: [Player 218, portrayedBy, Park Hae-soo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Park Hae-soo Context triple: [Player 218, portrayedBy, Park Hae-soo]
-
A.
Park Hae-soo
chosen
Park Hae-soo is a South Korean actor best known internationally for his prominent role in the hit Netflix survival drama series "Squid Game."
-
B.
Park Chan-ho
Park Chan-ho is a South Korean former Major League Baseball pitcher renowned as the first South Korean-born player in MLB and later a veteran presence in the KBO.
-
C.
Kim Sang-ho
Kim Sang-ho is a South Korean actor known for his versatile supporting roles in films and television dramas.
-
D.
Hong Joon-pyo
Hong Joon-pyo is a South Korean conservative politician who has served as governor of South Gyeongsang Province and as a prominent national lawmaker and party leader.
-
E.
Sung Dong-il
Sung Dong-il is a veteran South Korean actor known for his versatile supporting and character roles across popular films and television dramas.
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6679b179c8190a9511df8ed82098a |
completed | April 20, 2026, 5:51 p.m. |
Created at: April 11, 2026, 11:32 p.m.