Triple

T10574738
Position Surface form Disambiguated ID Type / Status
Subject Line 2 (Barcelona Metro) E249579 entity
Predicate shortName P43 FINISHED
Object L2
L2 is the commonly used designation for Line 2 of the Barcelona Metro, a rapid transit line serving several central and northern districts of the city.
E871699 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: L2 | Statement: [Line 2 (Barcelona Metro), shortName, L2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: L2
Context triple: [Line 2 (Barcelona Metro), shortName, L2]
  • A. L2
    L2 is the common shorthand for Ligue 2, the second tier of professional football in the French league system.
  • B. L2
    L2 is the second Sun–Earth Lagrange point, a gravitationally stable location in space used by space telescopes such as the James Webb Space Telescope for observation.
  • C. L2M
    L2M is a DARPA research initiative focused on developing AI systems capable of continuous, lifelong learning and adaptation.
  • D. L3
    L3 is a particle physics experiment that operated at CERN’s Large Electron–Positron Collider, designed to study high-energy electron–positron collisions and probe the Standard Model.
  • E. L
    L is the enigmatic, emotionally complex protagonist of Hanne Ørstavik’s novel "Love," whose inner life and perspective drive the story’s exploration of isolation and longing.
  • 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: L2
Triple: [Line 2 (Barcelona Metro), shortName, L2]
Generated description
L2 is the commonly used designation for Line 2 of the Barcelona Metro, a rapid transit line serving several central and northern districts of the city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: L2
Target entity description: L2 is the commonly used designation for Line 2 of the Barcelona Metro, a rapid transit line serving several central and northern districts of the city.
  • A. L2
    L2 is the second Sun–Earth Lagrange point, a gravitationally stable location in space used by space telescopes such as the James Webb Space Telescope for observation.
  • B. L2
    L2 is the common shorthand for Ligue 2, the second tier of professional football in the French league system.
  • C. L2M
    L2M is a DARPA research initiative focused on developing AI systems capable of continuous, lifelong learning and adaptation.
  • D. L3
    L3 is a particle physics experiment that operated at CERN’s Large Electron–Positron Collider, designed to study high-energy electron–positron collisions and probe the Standard Model.
  • E. L
    L is the enigmatic, emotionally complex protagonist of Hanne Ørstavik’s novel "Love," whose inner life and perspective drive the story’s exploration of isolation and longing.
  • 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_69d381c8bd708190acf3d275c908251e completed April 6, 2026, 9:50 a.m.
NER Named-entity recognition batch_69d52749dda08190b0c9627a931c5848 completed April 7, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69d94b5d89748190bb398943e4a16e9b completed April 10, 2026, 7:11 p.m.
NEDg Description generation batch_69d94e1502108190a81bfa1d5a425e5a completed April 10, 2026, 7:23 p.m.
NED2 Entity disambiguation (via description) batch_69d94f0bb6888190b4038df6dcd96d33 completed April 10, 2026, 7:27 p.m.
Created at: April 6, 2026, 12:38 p.m.