Triple

T13143832
Position Surface form Disambiguated ID Type / Status
Subject Empresa de los Ferrocarriles del Estado E312284 entity
Predicate shortName P43 FINISHED
Object EFE
EFE is Chile’s state-owned railway company responsible for operating and managing much of the country’s passenger and freight rail network.
 E1025046 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: EFE | Statement: [Empresa de los Ferrocarriles del Estado, shortName, EFE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: EFE
Context triple: [Empresa de los Ferrocarriles del Estado, shortName, EFE]
  • A. Efejoku
    Efejoku is a popular Nigerian street-hop song by rapper Lil Kesh, known for its energetic beat and club-friendly vibe.
  • B. Meduza
    Meduza is an Italian electronic music production trio best known for their chart-topping house tracks like "Piece of Your Heart" and "Lose Control."
  • C. EDFE
    EDFE is the ICAO airport code for Frankfurt Egelsbach Airport, a general aviation airfield near Frankfurt, Germany.
  • D. EFRO
    EFRO is the ICAO airport code for Rovaniemi Air Base in Finland, which serves both military and civilian air traffic near the Arctic Circle.
  • E. Ansa
    Ansa was a Lombard queen consort of the 8th century, known as the wife of King Desiderius and a significant political and religious patron in the Lombard kingdom.
  • 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: EFE
Triple: [Empresa de los Ferrocarriles del Estado, shortName, EFE]
Generated description
EFE is Chile’s state-owned railway company responsible for operating and managing much of the country’s passenger and freight rail network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: EFE
Target entity description: EFE is Chile’s state-owned railway company responsible for operating and managing much of the country’s passenger and freight rail network.
  • A. Efejoku
    Efejoku is a popular Nigerian street-hop song by rapper Lil Kesh, known for its energetic beat and club-friendly vibe.
  • B. Meduza
    Meduza is an Italian electronic music production trio best known for their chart-topping house tracks like "Piece of Your Heart" and "Lose Control."
  • C. EDFE
    EDFE is the ICAO airport code for Frankfurt Egelsbach Airport, a general aviation airfield near Frankfurt, Germany.
  • D. EFRO
    EFRO is the ICAO airport code for Rovaniemi Air Base in Finland, which serves both military and civilian air traffic near the Arctic Circle.
  • E. Ansa
    Ansa was a Lombard queen consort of the 8th century, known as the wife of King Desiderius and a significant political and religious patron in the Lombard kingdom.
  • 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_69d806aabde48190899e13e41659cae5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98bce3678819082a7aa1d83f20592 completed April 10, 2026, 11:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6eae4a87881908be57e15f001c904 completed May 3, 2026, 6:27 a.m.
NEDg Description generation batch_69f6f05a3dd48190a8b2c52f64a2edd0 completed May 3, 2026, 6:51 a.m.
NED2 Entity disambiguation (via description) batch_69f6f1a7c8108190a9668d2f0bb634b1 completed May 3, 2026, 6:56 a.m.
Created at: April 9, 2026, 9:10 p.m.