Triple

T12427802
Position Surface form Disambiguated ID Type / Status
Subject Vielha e Mijaran E296942 entity
Predicate hasSettlement P1068 FINISHED
Object Mont
Mont is a small village in the municipality of Vielha e Mijaran in the Val d'Aran region of Catalonia, Spain.
E983894 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: Mont | Statement: [Vielha e Mijaran, hasSettlement, Mont]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mont
Context triple: [Vielha e Mijaran, hasSettlement, Mont]
  • A. Mont
    Mont is a family surname associated with the character Michael Mont.
  • B. Columbia
    Columbia is an outdoor apparel and footwear brand known for its durable, weather-resistant gear for activities like hiking, skiing, and camping.
  • C. Columbia
    Columbia is a mid-sized city in central Missouri known as a major college town and cultural hub, home to the University of Missouri and several other educational institutions.
  • D. Columbia
    Columbia is the capital city of South Carolina, known for hosting the University of South Carolina and its prominent Gamecocks athletic programs.
  • E. Columbia
    Columbia is a small unincorporated community located in the state of Iowa in the United States.
  • 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: Mont
Triple: [Vielha e Mijaran, hasSettlement, Mont]
Generated description
Mont is a small village in the municipality of Vielha e Mijaran in the Val d'Aran region of Catalonia, Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mont
Target entity description: Mont is a small village in the municipality of Vielha e Mijaran in the Val d'Aran region of Catalonia, Spain.
  • A. Mont
    Mont is a family surname associated with the character Michael Mont.
  • B. Columbia
    Columbia was the former name of the John F. Kennedy/University of Massachusetts Boston subway station on Boston’s MBTA Red Line.
  • C. Columbia
    Columbia is a mid-sized city in central Missouri known as a major college town and cultural hub, home to the University of Missouri and several other educational institutions.
  • D. Columbia
    Columbia is a major American record label known for signing and releasing music by many influential artists across genres.
  • E. Columbia
    Columbia is a small unincorporated community located in the state of Iowa in the United States.
  • 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_69d6ada0640c81908c061d7fb3d47786 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d7ccda08190be2ff1739c1c6855 completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63f049c9c81908d870b0ee05f2d7e completed May 2, 2026, 6:14 p.m.
NEDg Description generation batch_69f640b513488190893359e9964dbe98 completed May 2, 2026, 6:21 p.m.
NED2 Entity disambiguation (via description) batch_69f641abe114819093d99a327f2220c2 completed May 2, 2026, 6:25 p.m.
Created at: April 8, 2026, 9:55 p.m.