Triple
T28193767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seoul–Busan |
E716382
|
entity |
| Predicate | roleInUrbanSystem |
P8652
|
FINISHED |
| Object | links political capital with largest port city |
—
|
LITERAL 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: links political capital with largest port city | Statement: [Seoul–Busan, roleInUrbanSystem, links political capital with largest port city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInUrbanSystem Context triple: [Seoul–Busan, roleInUrbanSystem, links political capital with largest port city]
-
A.
urbanRole
chosen
Indicates the function, status, or role that an entity holds within an urban or city context.
-
B.
roleInUrbanPlan
Indicates the specific function, responsibility, or position an entity holds within an urban planning context or scheme.
-
C.
hasUrbanRole
Indicates that an entity plays a specific functional or social role within an urban or city context.
-
D.
focusCityRole
Indicates that a city serves a particular primary role or function within a broader geographic or organizational context.
-
E.
hasCityRole
Indicates that an entity holds or is assigned a specific role, function, or status within a particular city.
- 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_69efd6b612f48190a72012b520afbd10 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69fdee770af48190aca2670db50f8b49 |
completed | May 8, 2026, 2:08 p.m. |
| PD | Predicate disambiguation | batch_69fdecec98a08190a357d816dc2a6dbe |
completed | May 8, 2026, 2:02 p.m. |
Created at: April 27, 2026, 10:26 p.m.