Triple
T14241510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwanak-gu |
E353016
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object | Guro-gu |
E1026366
|
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: Guro-gu | Statement: [Gwanak-gu, hasNeighbour, Guro-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guro-gu Context triple: [Gwanak-gu, hasNeighbour, Guro-gu]
-
A.
Guro-gu
chosen
Guro-gu is a district in southwestern Seoul, South Korea, known for its industrial areas, digital technology clusters, and dense urban residential neighborhoods.
-
B.
Yongsan-gu
Yongsan-gu is a central district of Seoul, South Korea, known for its diverse neighborhoods, major transportation hubs, and significant commercial and cultural centers.
-
C.
Dongdaemun-gu
Dongdaemun-gu is a central district in Seoul, South Korea, known for its major commercial areas, historic sites, and the iconic Dongdaemun Design Plaza.
-
D.
Eunpyeong-gu
Eunpyeong-gu is a district in northwestern Seoul, South Korea, known for its mix of urban residential areas and access to nearby mountains and temples.
-
E.
Yeongdeungpo District
Yeongdeungpo District is a major administrative and commercial area in southwestern Seoul, South Korea, known for its government institutions, business centers, and dense urban development.
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6244ad188190b9d9db7914240410 |
completed | April 14, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdd5bb9a5881908eac6153c7eb623e |
completed | May 8, 2026, 12:23 p.m. |
Created at: April 10, 2026, 1:08 a.m.