Triple
T5155053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kang Sae-byeok |
E116287
|
entity |
| Predicate | hasAlly |
P600
|
FINISHED |
| Object | Ji-yeong |
E496448
|
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: Ji-yeong | Statement: [Kang Sae-byeok, hasAlly, Ji-yeong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ji-yeong Context triple: [Kang Sae-byeok, hasAlly, Ji-yeong]
-
A.
Ji-yeong
chosen
Ji-yeong is a tragic supporting character in the Netflix series "Squid Game," known for her poignant friendship with Kang Sae-byeok and her self-sacrificial choice during the marbles game.
-
B.
Jihae
Jihae is a South Korean singer, songwriter, and actress known for her role in the National Geographic science-fiction series "Mars."
-
C.
Soyeon
Soyeon is a Korean-born interpreter and the wife of former German Chancellor Gerhard Schröder.
-
D.
Han Mi-nyeo
Han Mi-nyeo is a loud, manipulative, and opportunistic contestant in the South Korean survival drama series "Squid Game," known for her volatile alliances and dramatic personality.
-
E.
Jinwicheon
Jinwicheon is a river flowing through the city of Pyeongtaek in South Korea.
- 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_69bd445d94788190b72e2cc563120995 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd78ddb01081909457ff4208eac4d7 |
completed | March 20, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed00e333881908487be6958e5133c |
completed | March 21, 2026, 5:06 p.m. |
Created at: March 20, 2026, 1:44 p.m.