Triple

T9522477
Position Surface form Disambiguated ID Type / Status
Subject Allende metro station E229675 entity
Predicate hasStationCode P1289 FINISHED
Object ALN
ALN is the station code for Allende, a Mexico City Metro station on Line 2 in the historic center of Mexico City.
 E803918 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: ALN | Statement: [Allende metro station, hasStationCode, ALN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ALN
Context triple: [Allende metro station, hasStationCode, ALN]
  • A. ALRN
    ALRN is the commonly used abbreviation for the Legislative Assembly of the Brazilian state of Rio Grande do Norte, its unicameral state legislature.
  • B. ALC
    ALC is the IATA airport code for Alicante–Elche Airport, the main international airport serving Spain’s Costa Blanca region.
  • C. ALNY
    ALNY is the stock ticker symbol for Alnylam Pharmaceuticals, a biotechnology company specializing in RNA interference (RNAi) therapeutics.
  • D. ALV
    ALV is the stock ticker symbol for Allianz SE, a major global financial services and insurance company based in Germany.
  • E. Al
    Al is a common shortened form of given names such as Albert, Alan, or Alexander.
  • 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: ALN
Triple: [Allende metro station, hasStationCode, ALN]
Generated description
ALN is the station code for Allende, a Mexico City Metro station on Line 2 in the historic center of Mexico City.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ALN
Target entity description: ALN is the station code for Allende, a Mexico City Metro station on Line 2 in the historic center of Mexico City.
  • A. ALRN
    ALRN is the commonly used abbreviation for the Legislative Assembly of the Brazilian state of Rio Grande do Norte, its unicameral state legislature.
  • B. ALC
    ALC is the IATA airport code for Alicante–Elche Airport, the main international airport serving Spain’s Costa Blanca region.
  • C. ALNY
    ALNY is the stock ticker symbol for Alnylam Pharmaceuticals, a biotechnology company specializing in RNA interference (RNAi) therapeutics.
  • D. ALV
    ALV is the stock ticker symbol for Allianz SE, a major global financial services and insurance company based in Germany.
  • E. Al
    Al is a common shortened form of given names such as Albert, Alan, or Alexander.
  • 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_69ca847870a881909d8d751a7d29da39 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd989788e4819086c235bf37a56b04 completed April 1, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69d13a5cd2d881908b27649b396c50a1 completed April 4, 2026, 4:20 p.m.
NEDg Description generation batch_69d13be79b1c8190a9110312ae25cf32 completed April 4, 2026, 4:27 p.m.
NED2 Entity disambiguation (via description) batch_69d13ca165b88190b4d629df0e079b3b completed April 4, 2026, 4:30 p.m.
Created at: March 30, 2026, 7:59 p.m.