Triple

T4691387
Position Surface form Disambiguated ID Type / Status
Subject Lisbon public transport network E104040 entity
Predicate includesMajorInterchange P21867 FINISHED
Object Campo Grande
Campo Grande is a major transport hub in Lisbon that serves as a key connection point for metro, bus, and other public transit services.
 E460975 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: Campo Grande | Statement: [Lisbon public transport network, includesMajorInterchange, Campo Grande]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Campo Grande
Context triple: [Lisbon public transport network, includesMajorInterchange, Campo Grande]
  • A. Campo Grande
    Campo Grande is a neighborhood in the city of Recife, Brazil.
  • B. Campo Grande
    Campo Grande is the capital city of Brazil’s Mato Grosso do Sul state and a key urban and transportation hub for visitors heading into the Pantanal wetlands.
  • C. Barueri
    Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
  • D. Mourão
    Mourão is a small municipality in Portugal’s Alentejo region, known for its historic castle and proximity to the Alqueva Reservoir.
  • E. Jaraguá do Sul
    Jaraguá do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage and industrial economy.
  • 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: Campo Grande
Triple: [Lisbon public transport network, includesMajorInterchange, Campo Grande]
Generated description
Campo Grande is a major transport hub in Lisbon that serves as a key connection point for metro, bus, and other public transit services.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Campo Grande
Target entity description: Campo Grande is a major transport hub in Lisbon that serves as a key connection point for metro, bus, and other public transit services.
  • A. Campo Grande
    Campo Grande is a neighborhood in the city of Recife, Brazil.
  • B. Campo Grande
    Campo Grande is the capital city of Brazil’s Mato Grosso do Sul state and a key urban and transportation hub for visitors heading into the Pantanal wetlands.
  • C. Barueri
    Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
  • D. Mourão
    Mourão is a small municipality in Portugal’s Alentejo region, known for its historic castle and proximity to the Alqueva Reservoir.
  • E. Jaraguá do Sul
    Jaraguá do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage and industrial economy.
  • 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_69bd43df91f481908e9add1b617b60ef completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd6c3d1cb88190a42919dcbfe2568c completed March 20, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69be03bd47b08190a3d2a174eb2f7b2e completed March 21, 2026, 2:34 a.m.
NEDg Description generation batch_69be055390c08190a85f92aec34b390a completed March 21, 2026, 2:41 a.m.
NED2 Entity disambiguation (via description) batch_69be05d5bd008190bd94bd70092e8dfd completed March 21, 2026, 2:43 a.m.
Created at: March 20, 2026, 1:16 p.m.