Triple
T16106087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gook |
E390741
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Gook |
E390741
|
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: Gook | Statement: [Gook, title, Gook]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gook Context triple: [Gook, title, Gook]
-
A.
Gook
chosen
Gook is a 2017 independent drama film by Justin Chon that follows two Korean American brothers and a young Black girl during the 1992 Los Angeles riots, exploring racial tension, friendship, and identity.
-
B.
Gukmun
Gukmun is an old Korean term referring to the native Korean writing system that later came to be known as Joseongeul or Hangul.
-
C.
Gwak
Gwak is a Jarawan Bantu language spoken by a small community in Nigeria.
-
D.
Sook
Sook is the elderly, eccentric, and deeply kind cousin who serves as the narrator’s beloved companion in Truman Capote’s autobiographical short story “A Christmas Memory.”
-
E.
Gukje Sijang
Gukje Sijang is one of South Korea’s largest and most famous traditional markets, located in Busan and known for its wide variety of goods and bustling atmosphere.
- 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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff6d81d081909e1315f4dbfd7369 |
completed | April 17, 2026, 9:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeba1e4c08190a90f5102e0038056 |
completed | May 10, 2026, 2:21 a.m. |
Created at: April 10, 2026, 5 a.m.