Triple

T10102747
Position Surface form Disambiguated ID Type / Status
Subject Antero de Quental metro station E216241 entity
Predicate hasStationCode P1289 FINISHED
Object AQN
AQN is the station code for Lisbon Metro’s Antero de Quental station on the city’s urban rail network.
 E840589 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: AQN | Statement: [Antero de Quental metro station, hasStationCode, AQN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: AQN
Context triple: [Antero de Quental metro station, hasStationCode, AQN]
  • A. ANQ
    ANQ is the commonly used acronym for the National Assembly of Quebec, the unicameral legislative body of the Canadian province of Quebec.
  • B. QAN
    QAN is the Amtrak station code for the Quantico train station in Quantico, Virginia.
  • C. QAN
    QAN is the Australian Securities Exchange (ASX) stock ticker for Qantas Airways Limited, Australia’s flagship airline.
  • D. AQ
    AQ is an Oracle Database feature that provides message-based communication and queuing capabilities for building asynchronous, distributed applications.
  • E. AQJ
    AQJ is the IATA airport code for King Hussein International Airport serving Aqaba, Jordan.
  • 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: AQN
Triple: [Antero de Quental metro station, hasStationCode, AQN]
Generated description
AQN is the station code for Lisbon Metro’s Antero de Quental station on the city’s urban rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: AQN
Target entity description: AQN is the station code for Lisbon Metro’s Antero de Quental station on the city’s urban rail network.
  • A. ANQ
    ANQ is the commonly used acronym for the National Assembly of Quebec, the unicameral legislative body of the Canadian province of Quebec.
  • B. QAN
    QAN is the Amtrak station code for the Quantico train station in Quantico, Virginia.
  • C. QAN
    QAN is the Australian Securities Exchange (ASX) stock ticker for Qantas Airways Limited, Australia’s flagship airline.
  • D. AQ
    AQ is an Oracle Database feature that provides message-based communication and queuing capabilities for building asynchronous, distributed applications.
  • E. AQJ
    AQJ is the IATA airport code for King Hussein International Airport serving Aqaba, Jordan.
  • 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_69ca83d039f08190b9d10363221c69fb completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cdd09af07c819099774af46ebf62d7 completed April 2, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b6dcca848190851f6f1968fe244c completed April 5, 2026, 7:24 p.m.
NEDg Description generation batch_69d2b7e64fcc8190805a6a31c8a3786f completed April 5, 2026, 7:28 p.m.
NED2 Entity disambiguation (via description) batch_69d2b883959481909f330a26863621ae completed April 5, 2026, 7:31 p.m.
Created at: March 30, 2026, 9:02 p.m.