Triple
T29821374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seomyeon commercial district |
E757252
|
entity |
| Predicate | isOneOfBusiestAreasIn |
P173402
|
FINISHED |
| Object | Busan |
—
|
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: Busan | Statement: [Seomyeon commercial district, isOneOfBusiestAreasIn, Busan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isOneOfBusiestAreasIn Context triple: [Seomyeon commercial district, isOneOfBusiestAreasIn, Busan]
-
A.
isOneOfMostDenselyPopulatedAreasIn
Indicates that an area ranks among the locations with the highest population density within a specified region or context.
-
B.
isOneOfBusiestStopsOn
Indicates that a stop ranks among the most heavily used or frequently served stops on a given route or line.
-
C.
oneOfBusiestStationsIn
Indicates that a station is among the busiest stations within a specified area or system.
-
D.
isOneOfLargestMetropolitanAreasIn
Indicates that an entity ranks among the largest metropolitan areas within a specified geographic region or jurisdiction.
-
E.
isPrimaryCommercialAreaOf
Indicates that one area serves as the main center of commercial activity for another specified place or region.
- F. None of above. chosen
Provenance (4 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_69f2245701c88190ad42415a0956c4ed |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6b56ed31481908c3e5d749e46bad9 |
completed | May 3, 2026, 2:39 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a5fd8481909433e923c5e24e55 |
completed | May 3, 2026, 2:32 a.m. |
| PDg | Predicate description generation | batch_69f6b49339048190b617a6749f648825 |
completed | May 3, 2026, 2:36 a.m. |
Created at: April 29, 2026, 5:29 p.m.