Triple
T20257971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baekje Historic Areas |
E498753
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Iksan |
—
|
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: Iksan | Statement: [Baekje Historic Areas, locatedIn, Iksan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iksan Context triple: [Baekje Historic Areas, locatedIn, 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.
Sariwon
Sariwon is a major city in southwestern North Korea known as an administrative, transportation, and agricultural center.
-
C.
Ishkashimi
Ishkashimi is a lesser-known Eastern Iranian language spoken by small communities in parts of Afghanistan and Tajikistan.
-
D.
Kiga
Kiga is a Bantu language spoken primarily by the Bakiga people of southwestern Uganda, near the Great Lakes region of East Africa.
-
E.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
- 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e674c7296c819092860942de8f28d5 |
completed | April 20, 2026, 6:47 p.m. |
Created at: April 11, 2026, 11:41 p.m.