Triple

T7584692
Position Surface form Disambiguated ID Type / Status
Subject arrondissement of Draguignan E179576 entity
Predicate contains P35 FINISHED
Object Salernes
Salernes is a commune in southeastern France’s Var department, noted for its traditional ceramic tile production and Provençal village character.
 E674476 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: Salernes | Statement: [arrondissement of Draguignan, contains, Salernes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Salernes
Context triple: [arrondissement of Draguignan, contains, Salernes]
  • A. Solør
    Solør is a traditional district in Eastern Norway known for its rural landscapes, forestry, and agriculture.
  • B. Trondenes
    Trondenes is a historic former municipality and parish in northern Norway, known for its medieval stone church and role as an administrative center in the Harstad region.
  • C. Skudeneshavn
    Skudeneshavn is a historic coastal town in southwestern Norway known for its well-preserved wooden architecture and maritime heritage.
  • D. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • E. Suldal
    Suldal is a large rural municipality in southwestern Norway known for its fjords, mountains, and hydroelectric power production.
  • 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: Salernes
Triple: [arrondissement of Draguignan, contains, Salernes]
Generated description
Salernes is a commune in southeastern France’s Var department, noted for its traditional ceramic tile production and Provençal village character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Salernes
Target entity description: Salernes is a commune in southeastern France’s Var department, noted for its traditional ceramic tile production and Provençal village character.
  • A. Solør
    Solør is a traditional district in Eastern Norway known for its rural landscapes, forestry, and agriculture.
  • B. Trondenes
    Trondenes is a historic former municipality and parish in northern Norway, known for its medieval stone church and role as an administrative center in the Harstad region.
  • C. Skudeneshavn
    Skudeneshavn is a historic coastal town in southwestern Norway known for its well-preserved wooden architecture and maritime heritage.
  • D. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • E. Suldal
    Suldal is a large rural municipality in southwestern Norway known for its fjords, mountains, and hydroelectric power production.
  • 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_69c69f335248819093c1006f30513708 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f993cd0c8190864f801074625a32 completed March 27, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69c861812e08819097fd14fe2b8fee13 completed March 28, 2026, 11:17 p.m.
NEDg Description generation batch_69c862466fd481908ea5772e76a88d95 completed March 28, 2026, 11:20 p.m.
NED2 Entity disambiguation (via description) batch_69c862cae0448190859a07db338e1de7 completed March 28, 2026, 11:22 p.m.
Created at: March 27, 2026, 3:52 p.m.