Triple
T18631290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Jeolla Province |
E455422
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Iksan |
E649176
|
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: Iksan | Statement: [North Jeolla Province, hasCity, Iksan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iksan Context triple: [North Jeolla Province, hasCity, Iksan]
-
A.
Iksan
chosen
Iksan is a city in South Korea’s North Jeolla Province known as a key transportation hub and historical center with significant Baekje-era cultural heritage.
-
B.
Ishkashimi
Ishkashimi is a lesser-known Eastern Iranian language spoken by small communities in parts of Afghanistan and Tajikistan.
-
C.
Kiga
Kiga is a Bantu language spoken primarily by the Bakiga people of southwestern Uganda, near the Great Lakes region of East Africa.
-
D.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
-
E.
Kihoku
Kihoku is a town in Mie Prefecture, Japan, known for its coastal scenery and fishing industry along the Kumano Sea.
- 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fc4c5648190b771e9b080e98c15 |
completed | April 19, 2026, 9:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a050d7e812c8190ab055062a4479474 |
completed | May 13, 2026, 11:47 p.m. |
Created at: April 10, 2026, 11:46 a.m.