Triple

T27431203
Position Surface form Disambiguated ID Type / Status
Subject Gwangsan District E690641 entity
Predicate servedBy P82 FINISHED
Object Gwangju urban bus network
The Gwangju urban bus network is the primary public transit system of Gwangju, South Korea, providing extensive bus services that connect its districts and surrounding areas.
 E1780222 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: Gwangju urban bus network | Statement: [Gwangsan District, servedBy, Gwangju urban bus network]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Gwangju urban bus network
Triple: [Gwangsan District, servedBy, Gwangju urban bus network]
Generated description
The Gwangju urban bus network is the primary public transit system of Gwangju, South Korea, providing extensive bus services that connect its districts and surrounding areas.

Provenance (5 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_69ef52003fb48190b0f1295246182a86 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d59a6e88190a9042398d754c304 completed May 2, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12d0bd144481908ca7c7aba724ad9e completed May 24, 2026, 10:19 a.m.
NEDg Description generation batch_6a12d1497cb4819085e9a1a5401d9118 completed May 24, 2026, 10:22 a.m.
NED2 Entity disambiguation (via description) batch_6a12d2747f6881909aa2a5b0c389a494 completed May 24, 2026, 10:27 a.m.
Created at: April 27, 2026, 12:42 p.m.