Triple

T22456184
Position Surface form Disambiguated ID Type / Status
Subject Valle de Tena E555122 entity
Predicate hasTown P847 FINISHED
Object Panticosa
Panticosa is a mountain town and ski resort in the Spanish Pyrenees, known for its alpine scenery and historic thermal spa.
 E1537951 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: Panticosa | Statement: [Valle de Tena, hasTown, Panticosa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Panticosa
Context triple: [Valle de Tena, hasTown, Panticosa]
  • A. Beinasco
    Beinasco is a municipality in the Metropolitan City of Turin in the Piedmont region of northern Italy.
  • B. La Campa
    La Campa is a small rural municipality in western Honduras known for its traditional Lenca culture and colonial-era church.
  • C. Pasuquin
    Pasuquin is a coastal municipality in the province of Ilocos Norte in the Philippines, known for its salt-making industry and scenic beaches.
  • D. Jaca
    Jaca is a town in northeastern Spain, in the Pyrenees of Aragon, known as a mountain resort and former candidate host city for the Winter Olympics.
  • E. Betancuria
    Betancuria is a historic inland town and former capital of the Canary Island of Fuerteventura, known for its traditional architecture and rural charm.
  • 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: Panticosa
Triple: [Valle de Tena, hasTown, Panticosa]
Generated description
Panticosa is a mountain town and ski resort in the Spanish Pyrenees, known for its alpine scenery and historic thermal spa.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Panticosa
Target entity description: Panticosa is a mountain town and ski resort in the Spanish Pyrenees, known for its alpine scenery and historic thermal spa.
  • A. Beinasco
    Beinasco is a municipality in the Metropolitan City of Turin in the Piedmont region of northern Italy.
  • B. La Campa
    La Campa is a small rural municipality in western Honduras known for its traditional Lenca culture and colonial-era church.
  • C. Pasuquin
    Pasuquin is a coastal municipality in the province of Ilocos Norte in the Philippines, known for its salt-making industry and scenic beaches.
  • D. Jaca
    Jaca is a town in northeastern Spain, in the Pyrenees of Aragon, known as a mountain resort and former candidate host city for the Winter Olympics.
  • E. Betancuria
    Betancuria is a historic inland town and former capital of the Canary Island of Fuerteventura, known for its traditional architecture and rural charm.
  • 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_69e11e51fdec8190adfdf9f8a6362221 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15b4f19708190a50f29598fb1a204 completed April 29, 2026, 1:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b0c7fc40081909f27eb081ac156f1 completed May 18, 2026, 12:56 p.m.
NEDg Description generation batch_6a0b0ddb116c8190b19f9a01abb737e6 completed May 18, 2026, 1:02 p.m.
NED2 Entity disambiguation (via description) batch_6a0b0e4402e481909210c5feff3fcddf completed May 18, 2026, 1:04 p.m.
Created at: April 16, 2026, 8:48 p.m.