Triple

T4574659
Position Surface form Disambiguated ID Type / Status
Subject Pyrénées-Atlantiques E123114 entity
Predicate bordersDepartment P224 FINISHED
Object Landes
Landes is a department in southwestern France known for its vast Atlantic coastline, extensive pine forests, and popular surfing beaches.
 E453922 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: Landes | Statement: [Pyrénées-Atlantiques, bordersDepartment, Landes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Landes
Context triple: [Pyrénées-Atlantiques, bordersDepartment, Landes]
  • A. Rhegion
    Rhegion was an important ancient Greek city located at the southern tip of Italy, strategically positioned on the Strait of Messina.
  • B. Ille
    Ille is a small river in northwestern France that flows through the city of Rennes and joins the Vilaine River.
  • C. Valais
    Valais is a mountainous canton in southwestern Switzerland known for its Alpine scenery, vineyards, and popular ski resorts such as Zermatt and Verbier.
  • D. Tennengau
    Tennengau is a district in the Austrian state of Salzburg known for its alpine landscapes, historic salt mining heritage, and proximity to the city of Salzburg.
  • E. Pays d’Olmes
    Pays d’Olmes is a small mountainous area in the Ariège department of southwestern France, known for its textile heritage and proximity to the Pyrenees.
  • 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: Landes
Triple: [Pyrénées-Atlantiques, bordersDepartment, Landes]
Generated description
Landes is a department in southwestern France known for its vast Atlantic coastline, extensive pine forests, and popular surfing beaches.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Landes
Target entity description: Landes is a department in southwestern France known for its vast Atlantic coastline, extensive pine forests, and popular surfing beaches.
  • A. Rhegion
    Rhegion was an important ancient Greek city located at the southern tip of Italy, strategically positioned on the Strait of Messina.
  • B. Ille
    Ille is a small river in northwestern France that flows through the city of Rennes and joins the Vilaine River.
  • C. Valais
    Valais is a mountainous canton in southwestern Switzerland known for its Alpine scenery, vineyards, and popular ski resorts such as Zermatt and Verbier.
  • D. Tennengau
    Tennengau is a district in the Austrian state of Salzburg known for its alpine landscapes, historic salt mining heritage, and proximity to the city of Salzburg.
  • E. Pays d’Olmes
    Pays d’Olmes is a small mountainous area in the Ariège department of southwestern France, known for its textile heritage and proximity to the Pyrenees.
  • 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_69bd46466c7081909d07f36be2d08804 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd58c9f0bc81908d87f01ab067818a completed March 20, 2026, 2:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdd3dde41c81909adf91b53450e590 completed March 20, 2026, 11:10 p.m.
NEDg Description generation batch_69bdd79fe75c8190b672b80898d3cbf2 completed March 20, 2026, 11:26 p.m.
NED2 Entity disambiguation (via description) batch_69bdd85966348190841f272f347ee33f completed March 20, 2026, 11:29 p.m.
Created at: March 20, 2026, 1:10 p.m.