Triple
T7252578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baeggu language |
E157638
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object | Baegu |
E146329
|
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: Baegu | Statement: [Baeggu language, hasAlternativeName, Baegu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baegu Context triple: [Baeggu language, hasAlternativeName, Baegu]
-
A.
Baeggu
chosen
Baeggu is an Oceanic language of the Meso-Melanesian group spoken by a small community in the Solomon Islands.
-
B.
Seochon
Seochon is a historic neighborhood in central Seoul known for its traditional hanok houses, narrow alleyways, and vibrant mix of old Korean culture and modern cafes and galleries.
-
C.
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.
-
D.
Myeon
Myeon is a type of rural township-level administrative division used in South Korea.
-
E.
Yangju
Yangju is a city in northwestern South Korea known for its mix of suburban residential areas, light industry, and proximity to Seoul.
- 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_69c6882d81d4819085f7ff862951ee4f |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea7ae0e48190bd80c91bad1976c6 |
completed | March 27, 2026, 8:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7d3ac0de88190990c1ef25636260c |
completed | March 28, 2026, 1:12 p.m. |
Created at: March 27, 2026, 2:56 p.m.