Triple
T6535481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BIFF Square |
E152347
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Yeongnam |
E29096
|
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: Yeongnam | Statement: [BIFF Square, region, Yeongnam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yeongnam Context triple: [BIFF Square, region, Yeongnam]
-
A.
Anseongcheon
Anseongcheon is a river in South Korea that flows through the city of Pyeongtaek in Gyeonggi Province.
-
B.
Minho region
The Minho region is a historic and culturally rich area in northwest Portugal, known for its lush green landscapes, traditional cuisine, and production of Vinho Verde wine.
-
C.
Yeongnam region
chosen
The Yeongnam region is a major southeastern area of South Korea encompassing key cities such as Busan and Daegu, known for its dense population, industry, and distinct cultural identity.
-
D.
Myeongneung
Myeongneung is one of the royal burial sites from Korea’s Joseon Dynasty, forming part of the UNESCO-listed Royal Tombs complex.
-
E.
Nam-gu
Nam-gu is a central administrative district of the metropolitan city of Ulsan in South Korea, known for its residential areas, commercial centers, and proximity to major industrial complexes.
- 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_69c688048ec8819093a47f7d332e12ec |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6adc05da88190b402085954cec8e0 |
completed | March 27, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6e41b0a8881908aebef421a6d403c |
completed | March 27, 2026, 8:10 p.m. |
Created at: March 27, 2026, 1:46 p.m.