Triple

T16885814
Position Surface form Disambiguated ID Type / Status
Subject Padang Pariaman Regency E421535 entity
Predicate hasUrbanCenter P2106 FINISHED
Object Sicincin
Sicincin is a town in West Sumatra, Indonesia, known as one of the main urban centers of Padang Pariaman Regency.
E1238523 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: Sicincin | Statement: [Padang Pariaman Regency, hasUrbanCenter, Sicincin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sicincin
Context triple: [Padang Pariaman Regency, hasUrbanCenter, Sicincin]
  • A. Sica Sica
    Sica Sica is a small highland town in Bolivia known for its colonial architecture, hot springs, and role as an administrative center in the La Paz Department.
  • B. Cinca
    The Cinca is a major river in northeastern Spain that flows through the province of Huesca as a tributary of the Ebro.
  • C. Sangisari
    Sangisari is an Iranian language spoken by the Sangisari people in the Semnan Province of north-central Iran.
  • D. Sakia
    Sakia is a prominent cultural center and arts venue in Cairo, Egypt, known for hosting concerts, exhibitions, and a wide range of cultural events.
  • E. Sibulan
    Sibulan is a coastal municipality in the Philippine province of Negros Oriental known as a gateway to Dumaguete City and for its local airport and seaport.
  • 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: Sicincin
Triple: [Padang Pariaman Regency, hasUrbanCenter, Sicincin]
Generated description
Sicincin is a town in West Sumatra, Indonesia, known as one of the main urban centers of Padang Pariaman Regency.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Sicincin
Target entity description: Sicincin is a town in West Sumatra, Indonesia, known as one of the main urban centers of Padang Pariaman Regency.
  • A. Sica Sica
    Sica Sica is a small highland town in Bolivia known for its colonial architecture, hot springs, and role as an administrative center in the La Paz Department.
  • B. Cinca
    The Cinca is a major river in northeastern Spain that flows through the province of Huesca as a tributary of the Ebro.
  • C. Sangisari
    Sangisari is an Iranian language spoken by the Sangisari people in the Semnan Province of north-central Iran.
  • D. Sakia
    Sakia is a prominent cultural center and arts venue in Cairo, Egypt, known for hosting concerts, exhibitions, and a wide range of cultural events.
  • E. Sibulan
    Sibulan is a coastal municipality in the Philippine province of Negros Oriental known as a gateway to Dumaguete City and for its local airport and seaport.
  • 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_69d889d470fc8190b4aec199636c0c56 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e3bbc126e881909dae8133ad34acc9 completed April 18, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c2bcf290819098be9def471e02b8 completed May 10, 2026, 5:39 p.m.
NEDg Description generation batch_6a00c3c25e9481908327bb6646212368 completed May 10, 2026, 5:43 p.m.
NED2 Entity disambiguation (via description) batch_6a00c44e37b48190a62b315ddbbd4ec4 completed May 10, 2026, 5:45 p.m.
Created at: April 10, 2026, 5:29 a.m.