Triple

T12677234
Position Surface form Disambiguated ID Type / Status
Subject Linha da Beira Alta E302847 entity
Predicate terminus P388 FINISHED
Object Pampilhosa
Pampilhosa is a town in central Portugal known historically as an important railway junction on the country’s main inland routes.
 E996721 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: Pampilhosa | Statement: [Linha da Beira Alta, terminus, Pampilhosa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pampilhosa
Context triple: [Linha da Beira Alta, terminus, Pampilhosa]
  • A. Pampilhosa da Serra
    Pampilhosa da Serra is a small municipality in central Portugal known for its mountainous landscapes, schist villages, and forested river valleys.
  • B. Bemposta
    Bemposta is a civil parish located within the municipality of Abrantes in central Portugal.
  • C. Pinheiral
    Pinheiral is a small municipality in the state of Rio de Janeiro, Brazil, known for its rural character and growing role as a regional educational and residential center.
  • D. Raposeira
    Raposeira is a small village in Portugal’s Algarve region, known for its rural charm and proximity to the Atlantic coast near Vila do Bispo.
  • E. Parnamirim
    Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
  • 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: Pampilhosa
Triple: [Linha da Beira Alta, terminus, Pampilhosa]
Generated description
Pampilhosa is a town in central Portugal known historically as an important railway junction on the country’s main inland routes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pampilhosa
Target entity description: Pampilhosa is a town in central Portugal known historically as an important railway junction on the country’s main inland routes.
  • A. Pampilhosa da Serra
    Pampilhosa da Serra is a small municipality in central Portugal known for its mountainous landscapes, schist villages, and forested river valleys.
  • B. Bemposta
    Bemposta is a civil parish located within the municipality of Abrantes in central Portugal.
  • C. Pinheiral
    Pinheiral is a small municipality in the state of Rio de Janeiro, Brazil, known for its rural character and growing role as a regional educational and residential center.
  • D. Raposeira
    Raposeira is a small village in Portugal’s Algarve region, known for its rural charm and proximity to the Atlantic coast near Vila do Bispo.
  • E. Parnamirim
    Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
  • 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_69d7bdee64a08190801c6d470aefd723 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d961b0d9c88190a05d6cbcb7a1642d completed April 10, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f671a341288190822fae2469efea09 completed May 2, 2026, 9:50 p.m.
NEDg Description generation batch_69f672ac07908190bd2dfe90d55a13c1 completed May 2, 2026, 9:54 p.m.
NED2 Entity disambiguation (via description) batch_69f67360b530819085d5db2aa0b7513d completed May 2, 2026, 9:57 p.m.
Created at: April 9, 2026, 5:20 p.m.