Triple

T12341294
Position Surface form Disambiguated ID Type / Status
Subject Tacuba metro station E294230 entity
Predicate hasStationCode P1289 FINISHED
Object TAU
TAU is the station code for Tacuba, an interchange station on Mexico City’s Metro system.
 E978838 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: TAU | Statement: [Tacuba metro station, hasStationCode, TAU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TAU
Context triple: [Tacuba metro station, hasStationCode, TAU]
  • A. TAU
    TAU is a major public research university located in Tel Aviv, Israel, known for its strong programs across science, engineering, humanities, and the arts.
  • B. TAO
    TAO is the UN/LOCODE designation for the major Chinese seaport of Qingdao, a key hub for international maritime trade.
  • C. TAO
    TAO is the ICAO airline designator assigned to Aeromar, a regional airline based in Mexico.
  • D. Tau
    Tau is the 19th letter of the Greek alphabet, commonly used as a symbol in mathematics, physics, and engineering.
  • E. Tau
    Tau is the standard astronomical abbreviation for the constellation Taurus, used in star designations such as those of Sterope I.
  • 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: TAU
Triple: [Tacuba metro station, hasStationCode, TAU]
Generated description
TAU is the station code for Tacuba, an interchange station on Mexico City’s Metro system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TAU
Target entity description: TAU is the station code for Tacuba, an interchange station on Mexico City’s Metro system.
  • A. TAU
    TAU is a major public research university located in Tel Aviv, Israel, known for its strong programs across science, engineering, humanities, and the arts.
  • B. TAO
    TAO is the UN/LOCODE designation for the major Chinese seaport of Qingdao, a key hub for international maritime trade.
  • C. TAO
    TAO is the ICAO airline designator assigned to Aeromar, a regional airline based in Mexico.
  • D. Tau
    Tau is the 19th letter of the Greek alphabet, commonly used as a symbol in mathematics, physics, and engineering.
  • E. Tau
    Tau is the standard astronomical abbreviation for the constellation Taurus, used in star designations such as those of Sterope I.
  • 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_69d6ab6ccbec8190b09e2d357aa80064 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f7758dc8190bbc6a9ad00b01dce completed April 10, 2026, 6:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f62aaa1d548190be065412aab70385 completed May 2, 2026, 4:47 p.m.
NEDg Description generation batch_69f62c55aacc8190a0544306825bdfab completed May 2, 2026, 4:54 p.m.
NED2 Entity disambiguation (via description) batch_69f62d51ab8081909c6f534051019dca completed May 2, 2026, 4:58 p.m.
Created at: April 8, 2026, 9:53 p.m.