Triple

T1706009
Position Surface form Disambiguated ID Type / Status
Subject São Bento Palace E36873 entity
Predicate district P2709 FINISHED
Object Santos-o-Velho
Santos-o-Velho is a historic riverside neighborhood in Lisbon, Portugal, known for its old convents, palaces, and vibrant nightlife along the Tagus.
 E192281 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: Santos-o-Velho | Statement: [São Bento Palace, district, Santos-o-Velho]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Santos-o-Velho
Context triple: [São Bento Palace, district, Santos-o-Velho]
  • A. Ramos da Costa
    Ramos da Costa is a Portuguese-language surname associated with individuals such as Francisco Ramos da Costa.
  • B. Santo Antão
    Santo Antão is the westernmost and one of the largest islands of Cape Verde, renowned for its dramatic volcanic mountains, deep valleys, and lush hiking landscapes.
  • C. Armação de Pêra
    Armação de Pêra is a coastal resort town in Portugal’s Algarve region, known for its sandy beaches and tourism.
  • D. Abrantes
    Abrantes is a historic Portuguese city in the Santarém District, known for its hilltop castle and strategic location overlooking the Tagus River.
  • E. Sabrosa
    Sabrosa is a small municipality in Portugal’s Douro region, historically notable as the birthplace of explorer Ferdinand Magellan.
  • 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: Santos-o-Velho
Triple: [São Bento Palace, district, Santos-o-Velho]
Generated description
Santos-o-Velho is a historic riverside neighborhood in Lisbon, Portugal, known for its old convents, palaces, and vibrant nightlife along the Tagus.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Santos-o-Velho
Target entity description: Santos-o-Velho is a historic riverside neighborhood in Lisbon, Portugal, known for its old convents, palaces, and vibrant nightlife along the Tagus.
  • A. Ramos da Costa
    Ramos da Costa is a Portuguese-language surname associated with individuals such as Francisco Ramos da Costa.
  • B. Santo Antão
    Santo Antão is the westernmost and one of the largest islands of Cape Verde, renowned for its dramatic volcanic mountains, deep valleys, and lush hiking landscapes.
  • C. Armação de Pêra
    Armação de Pêra is a coastal resort town in Portugal’s Algarve region, known for its sandy beaches and tourism.
  • D. Abrantes
    Abrantes is a historic Portuguese city in the Santarém District, known for its hilltop castle and strategic location overlooking the Tagus River.
  • E. Sabrosa
    Sabrosa is a small municipality in Portugal’s Douro region, historically notable as the birthplace of explorer Ferdinand Magellan.
  • 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_69a88617439c819094ffb5d16a0f6307 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa62f52ea48190a176eb499f946301 completed March 6, 2026, 5:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad8ad3b9c481909f83f7045789e49d completed March 8, 2026, 2:42 p.m.
NEDg Description generation batch_69ad957778a0819080f7fee35f5d8ed5 completed March 8, 2026, 3:27 p.m.
NED2 Entity disambiguation (via description) batch_69ad97aa308c81909f245a2133fc471b completed March 8, 2026, 3:37 p.m.
Created at: March 4, 2026, 7:30 p.m.