Triple
T22872837
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FNJ |
E567242
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Sunan 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: Sunan District | Statement: [FNJ, locatedNear, Sunan District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sunan District Context triple: [FNJ, locatedNear, Sunan District]
-
A.
Sunan District
chosen
Sunan District is an administrative district of Pyongyang, North Korea, best known for hosting the capital’s main international airport.
-
B.
Kanda district
Kanda district is a historic commercial and cultural area in central Tokyo known for its old bookstores, electronics shops, and traditional shrines.
-
C.
Buka District
Buka District is an administrative district located within the Tashkent Region of Uzbekistan.
-
D.
Kaifu District
Kaifu District is an urban administrative district of Changsha, the capital city of Hunan Province in south-central China.
-
E.
Senju district
Senju district is a historic neighborhood in Adachi, Tokyo, known for its traditional shopping streets, residential areas, and role as a local commercial hub.
- 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_69e24589d8348190b96422d13a678bc1 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17f55c4b88190adb49871e496ca54 |
completed | April 29, 2026, 3:47 a.m. |
Created at: April 17, 2026, 3:38 p.m.