Triple

T2254515
Position Surface form Disambiguated ID Type / Status
Subject Limoges E49689 entity
Predicate twinCity P1072 FINISHED
Object Seto
Seto is a city in Kagawa Prefecture, Japan, known for its traditional ceramics and role as a regional cultural and industrial center.
 E248953 NE FINISHED

How this triple was built (4 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: Seto | Statement: [Limoges, twinCity, Seto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Seto
Context triple: [Limoges, twinCity, Seto]
  • A. Seto
    Seto is a South Estonian dialect and cultural variety spoken by the Seto people, known for its distinct linguistic features and rich folk traditions.
  • B. Nambui
    Nambui was a Mongol empress consort of the Yuan dynasty and a prominent wife of Kublai Khan, influential in the imperial court after the death of his first empress.
  • C. Shimamoto
    Shimamoto is a town in Osaka Prefecture, Japan, located between Kyoto and Osaka along the Yodo River.
  • D. Tatsuno
    Tatsuno is a city in western Japan known for its traditional soy sauce production and historic townscape within Hyogo Prefecture.
  • E. Yanam
    Yanam is a coastal town and district enclave of the Union Territory of Puducherry in India, historically influenced by French colonial rule and culturally linked to the Telugu-speaking region of Andhra Pradesh.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Seto
Triple: [Limoges, twinCity, Seto]
Generated description
Seto is a city in Kagawa Prefecture, Japan, known for its traditional ceramics and role as a regional cultural and industrial center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Seto
Target entity description: Seto is a city in Kagawa Prefecture, Japan, known for its traditional ceramics and role as a regional cultural and industrial center.
  • A. Seto
    Seto is a South Estonian dialect and cultural variety spoken by the Seto people, known for its distinct linguistic features and rich folk traditions.
  • B. Nambui
    Nambui was a Mongol empress consort of the Yuan dynasty and a prominent wife of Kublai Khan, influential in the imperial court after the death of his first empress.
  • C. Shimamoto
    Shimamoto is a town in Osaka Prefecture, Japan, located between Kyoto and Osaka along the Yodo River.
  • D. Tatsuno
    Tatsuno is a city in western Japan known for its traditional soy sauce production and historic townscape within Hyogo Prefecture.
  • E. Yanam
    Yanam is a coastal town and district enclave of the Union Territory of Puducherry in India, historically influenced by French colonial rule and culturally linked to the Telugu-speaking region of Andhra Pradesh.
  • F. None of above. chosen

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_69a88aaa9250819095e127d0d77e8a32 completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc121af78819085b2e601d2f9bcdf completed March 7, 2026, 6:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6b1fc8808190aebc534ea5adb534 completed March 9, 2026, 6:39 a.m.
NEDg Description generation batch_69ae6c87e3108190bc3852b3ebdba45a completed March 9, 2026, 6:45 a.m.
NED2 Entity disambiguation (via description) batch_69ae6d0f723c8190bf0f91dad961e377 completed March 9, 2026, 6:47 a.m.
Created at: March 4, 2026, 7:47 p.m.