Triple
T23436290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sungkyul University |
E563470
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Anyang |
—
|
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: Anyang | Statement: [Sungkyul University, locatedIn, Anyang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anyang Context triple: [Sungkyul University, locatedIn, Anyang]
-
A.
Anyang
Anyang is an ancient city in northern China renowned as one of the historical capitals of the Shang dynasty and a major archaeological site.
-
B.
Anyang
chosen
Anyang is a mid-sized South Korean city in the Seoul Capital Area known for its residential districts, light industry, and proximity to central Seoul.
-
C.
Sanhe City
Sanhe City is a county-level city in Hebei Province, China, located near Beijing and forming part of the Beijing–Tianjin–Hebei metropolitan region.
-
D.
Taian
Taian is a prefecture-level city in eastern China's Shandong province, best known as the gateway to the sacred Mount Tai.
-
E.
Hejin
Hejin is a county-level city in southern Shanxi Province, China, situated along the Fen River near its confluence with the Yellow River.
- 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_69e24553980c8190bb66a2ae0bdab125 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a5dbdf248190a09e971f2718d01f |
completed | April 29, 2026, 6:31 a.m. |
Created at: April 17, 2026, 5:50 p.m.