Triple
T21622760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheonggyecheon Stream |
E533618
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Jung District |
—
|
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: Jung District | Statement: [Cheonggyecheon Stream, locatedIn, Jung District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jung District Context triple: [Cheonggyecheon Stream, locatedIn, Jung District]
-
A.
Jung District
chosen
Jung District is a central administrative and commercial district in Seoul, South Korea, known for its major business centers, historic sites, and cultural landmarks.
-
B.
Jung District
Jung District is a central administrative and commercial district of Busan, South Korea, known for its historic markets, port-side location, and dense urban landscape.
-
C.
Jung District
Jung District is a central coastal district of Incheon, South Korea, known for encompassing Incheon International Airport and parts of the city’s historic port area.
-
D.
Jung District
Jung District is a central urban district of Daegu, South Korea, known as one of the city’s primary commercial and administrative hubs.
-
E.
Kang District
Kang District is an administrative district located in Nimruz Province in southwestern Afghanistan.
- 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_69e0c464fba881908d0ff2ac80511ce1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef3bb0c42c8190997fbeb7a764d60e |
completed | April 27, 2026, 10:34 a.m. |
Created at: April 16, 2026, 6:34 p.m.