Triple
T26523149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Busan metropolitan area |
E670012
|
entity |
| Predicate | hasUrbanRailTransit |
P64445
|
FINISHED |
| Object | Busan Metro |
—
|
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 Metro | Statement: [Busan metropolitan area, hasUrbanRailTransit, Busan Metro]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanRailTransit Context triple: [Busan metropolitan area, hasUrbanRailTransit, Busan Metro]
-
A.
isUrbanRailwayIn
chosen
Indicates that an urban railway system or line is located within or operates inside a specified geographic or administrative area.
-
B.
hasLightRailSystem
Indicates that a place possesses and operates a light rail transit system.
-
C.
isSuburbanRailway
Indicates that a railway line, service, or system operates primarily within and around a metropolitan area, typically connecting the city center with its suburbs.
-
D.
hasMajorPublicTransitSystem
Indicates that a place possesses a significant, widely used public transportation network (such as buses, subways, or trains) that serves a large portion of its population.
-
E.
hasTramway
Indicates that a location or area is served by, contains, or is connected to a tramway system.
- F. None of above.
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_69eeb31b6dcc8190b30632dc3928a0c0 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f6247480cc8190a887eedaeb94615c |
completed | May 2, 2026, 4:21 p.m. |
| PD | Predicate disambiguation | batch_69f623a7539c8190b71797f583da9f63 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 1:29 a.m.