Triple

T24965577
Position Surface form Disambiguated ID Type / Status
Subject Yeosu E624731 entity
Predicate hasAttraction P105 FINISHED
Object Yi Sun-sin Plaza
Yi Sun-sin Plaza is a waterfront public square and tourist attraction in Yeosu, South Korea, featuring monuments and scenic views commemorating the famed naval commander Admiral Yi Sun-sin.
E1662059 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: Yi Sun-sin Plaza | Statement: [Yeosu, hasAttraction, Yi Sun-sin Plaza]
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: Yi Sun-sin Plaza
Triple: [Yeosu, hasAttraction, Yi Sun-sin Plaza]
Generated description
Yi Sun-sin Plaza is a waterfront public square and tourist attraction in Yeosu, South Korea, featuring monuments and scenic views commemorating the famed naval commander Admiral Yi Sun-sin.

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_69e2ff24512481908e9a72315b8d0354 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f444d7f9e4819098276f05604b2f2a completed May 1, 2026, 6:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048a3533c8190b75553b97ed1b4a5 completed May 22, 2026, 12:14 p.m.
NEDg Description generation batch_6a10498ee91081909f400a590f3646a7 completed May 22, 2026, 12:18 p.m.
NED2 Entity disambiguation (via description) batch_6a104aa15f248190ba69524b7d516bc1 completed May 22, 2026, 12:22 p.m.
Created at: April 18, 2026, 6 a.m.