Triple

T2642408
Position Surface form Disambiguated ID Type / Status
Subject Faro railway station E62900 entity
Predicate hasStationCode P1289 FINISHED
Object FAR
FAR is the station code for Faro railway station, a key rail transport hub serving the city of Faro in southern Portugal’s Algarve region.
E285805 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: FAR | Statement: [Faro railway station, hasStationCode, FAR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: FAR
Context triple: [Faro railway station, hasStationCode, FAR]
  • A. FAR
    FAR is the acronym for Cuba’s national military organization, the Revolutionary Armed Forces.
  • B. FAR
    FAR is the commonly used abbreviation for the Intergovernmental Panel on Climate Change’s First Assessment Report, a foundational scientific evaluation of climate change published in 1990.
  • C. AS FAR
    AS FAR is a prominent Moroccan football club based in Rabat, known for its strong domestic record and intense rivalries in Moroccan football.
  • D. FRO
    FRO is the three-letter ISO 3166-1 alpha-3 country code assigned to the Faroe Islands.
  • E. FA
    FA is the governing body of association football in England, responsible for overseeing the national teams, competitions, and the rules of the game within the country.
  • 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: FAR
Triple: [Faro railway station, hasStationCode, FAR]
Generated description
FAR is the station code for Faro railway station, a key rail transport hub serving the city of Faro in southern Portugal’s Algarve region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: FAR
Target entity description: FAR is the station code for Faro railway station, a key rail transport hub serving the city of Faro in southern Portugal’s Algarve region.
  • A. FAR
    FAR is the acronym for Cuba’s national military organization, the Revolutionary Armed Forces.
  • B. FAR
    FAR is the commonly used abbreviation for the Intergovernmental Panel on Climate Change’s First Assessment Report, a foundational scientific evaluation of climate change published in 1990.
  • C. AS FAR
    AS FAR is a prominent Moroccan football club based in Rabat, known for its strong domestic record and intense rivalries in Moroccan football.
  • D. FRO
    FRO is the three-letter ISO 3166-1 alpha-3 country code assigned to the Faroe Islands.
  • E. FA
    FA is the governing body of association football in England, responsible for overseeing the national teams, competitions, and the rules of the game within the country.
  • 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_69ab4c3f2dcc819082df80f5e032f690 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abd8ff34988190ba9d69ce9d77c71d completed March 7, 2026, 7:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69af98bfd4008190a30675ebaf01e483 completed March 10, 2026, 4:06 a.m.
NEDg Description generation batch_69af99416924819099d4acb1a2d60e0c completed March 10, 2026, 4:08 a.m.
NED2 Entity disambiguation (via description) batch_69af99adadb08190a44f2286b25bf0aa completed March 10, 2026, 4:10 a.m.
Created at: March 6, 2026, 9:53 p.m.