Triple
T9293937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheonan |
E223590
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Asan |
E215809
|
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: Asan | Statement: [Cheonan, locatedNear, Asan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Asan Context triple: [Cheonan, locatedNear, Asan]
-
A.
Asan
chosen
Asan is a city in South Korea known for its hot springs, historical sites, and growing role as an industrial and educational center.
-
B.
Hasana
Hasana is a small town in Egypt’s North Sinai Governorate, situated in the Sinai Peninsula.
-
C.
Gasan
Gasan is a coastal municipality on the island province of Marinduque in the Philippines, known for its beaches, cultural festivals, and historic churches.
-
D.
Akhasheni
Akhasheni is a Georgian red wine appellation from the Kakheti region, known for its naturally semi-sweet wines made primarily from Saperavi grapes.
-
E.
Asmal
Asmal is a surname most notably associated with Kader Asmal, a prominent South African anti-apartheid activist, academic, and government minister.
- 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_69ca8423edb08190bc0c91287a484768 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd089a2ed0819086b2e4219ff2453e |
completed | April 1, 2026, 11:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0b241251c81909aa4e8bcf5cd9c2e |
completed | April 4, 2026, 6:40 a.m. |
Created at: March 30, 2026, 7:35 p.m.