Triple

T3730423
Position Surface form Disambiguated ID Type / Status
Subject Central Sulawesi E79046 entity
Predicate hasCity P316 FINISHED
Object Parigi
Parigi is a coastal town that serves as the administrative center of Parigi Moutong Regency in Central Sulawesi, Indonesia.
 E395777 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: Parigi | Statement: [Central Sulawesi, hasCity, Parigi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Parigi
Context triple: [Central Sulawesi, hasCity, Parigi]
  • A. Paris
    Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
  • B. Paris
    Paris is a prince of Troy in Greek mythology, best known for judging the beauty contest of the goddesses and for abducting Helen, which sparked the Trojan War.
  • C. Lyon
    Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
  • D. Boulogne-Billancourt
    Boulogne-Billancourt is a densely populated suburban city just southwest of central Paris, known as a major economic and media hub in the Île-de-France region.
  • E. Palaiseau
    Palaiseau is a suburban commune in the southern outskirts of Paris, France, known for hosting major scientific and engineering institutions.
  • 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: Parigi
Triple: [Central Sulawesi, hasCity, Parigi]
Generated description
Parigi is a coastal town that serves as the administrative center of Parigi Moutong Regency in Central Sulawesi, Indonesia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Parigi
Target entity description: Parigi is a coastal town that serves as the administrative center of Parigi Moutong Regency in Central Sulawesi, Indonesia.
  • A. Paris
    Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
  • B. Paris
    Paris is a prince of Troy in Greek mythology, best known for judging the beauty contest of the goddesses and for abducting Helen, which sparked the Trojan War.
  • C. Lyon
    Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
  • D. Boulogne-Billancourt
    Boulogne-Billancourt is a densely populated suburban city just southwest of central Paris, known as a major economic and media hub in the Île-de-France region.
  • E. Palaiseau
    Palaiseau is a suburban commune in the southern outskirts of Paris, France, known for hosting major scientific and engineering institutions.
  • 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_69ad8b0e4650819090ad7cef094285e8 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb1bb5408190990ea4dfbdab5c68 completed March 8, 2026, 7:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51211a34c8190a3cd8d45e8f7dd87 completed March 14, 2026, 7:45 a.m.
NEDg Description generation batch_69b515c97d8c81909c65ac2e82b2d3d1 completed March 14, 2026, 8:01 a.m.
NED2 Entity disambiguation (via description) batch_69b51684ce6c819098fd3f903cc75a05 completed March 14, 2026, 8:04 a.m.
Created at: March 8, 2026, 3:34 p.m.