Triple

T16972913
Position Surface form Disambiguated ID Type / Status
Subject Madrid metropolitan area E411731 entity
Predicate hasMotorway P385 FINISHED
Object A-1
A-1 is a major Spanish motorway that connects Madrid with the northern regions of the country, forming a key part of the national road network.
 E1242618 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: A-1 | Statement: [Madrid metropolitan area, hasMotorway, A-1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: A-1
Context triple: [Madrid metropolitan area, hasMotorway, A-1]
  • A. A-1
    A-1 is the station code assigned to Kabaty, the southern terminus of Warsaw Metro Line M1.
  • B. A 1
    A 1 is a major German autobahn that runs north–south across the country, connecting key cities and regions.
  • C. A1A
    A1A is a scenic coastal highway in Florida that runs along the Atlantic Ocean, known for connecting popular beaches and tourist destinations.
  • D. A-1M
    The A-1M is an upgraded version of the Brazilian AMX A-1 attack aircraft featuring modernized avionics, sensors, and systems to enhance its ground-attack and reconnaissance capabilities.
  • E. A-0
    A-0 is one of the earliest high-level programming language compilers, developed by Grace Hopper to automatically translate symbolic mathematical code into machine language.
  • 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: A-1
Triple: [Madrid metropolitan area, hasMotorway, A-1]
Generated description
A-1 is a major Spanish motorway that connects Madrid with the northern regions of the country, forming a key part of the national road network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: A-1
Target entity description: A-1 is a major Spanish motorway that connects Madrid with the northern regions of the country, forming a key part of the national road network.
  • A. A-1
    A-1 is the station code assigned to Kabaty, the southern terminus of Warsaw Metro Line M1.
  • B. A 1
    A 1 is a major German autobahn that runs north–south across the country, connecting key cities and regions.
  • C. A1A
    A1A is a scenic coastal highway in Florida that runs along the Atlantic Ocean, known for connecting popular beaches and tourist destinations.
  • D. A-1M
    The A-1M is an upgraded version of the Brazilian AMX A-1 attack aircraft featuring modernized avionics, sensors, and systems to enhance its ground-attack and reconnaissance capabilities.
  • E. A-0
    A-0 is one of the earliest high-level programming language compilers, developed by Grace Hopper to automatically translate symbolic mathematical code into machine language.
  • 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_69d886ca8f348190812768ea8d5055ce completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d0ae47f08190a13e98d20aba7f16 completed April 18, 2026, 6:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d4738fbc819099e8281ebc777091 completed May 10, 2026, 6:54 p.m.
NEDg Description generation batch_6a00d51835c48190b1a37de6ac25ceaa completed May 10, 2026, 6:57 p.m.
NED2 Entity disambiguation (via description) batch_6a00d59b96108190a0e55f01529a0b64 completed May 10, 2026, 6:59 p.m.
Created at: April 10, 2026, 5:31 a.m.