Triple

T8005381
Position Surface form Disambiguated ID Type / Status
Subject Ligue 2 E186351 entity
Predicate abbreviation P43 FINISHED
Object L2
L2 is the common shorthand for Ligue 2, the second tier of professional football in the French league system.
 E705397 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: [Ligue 2, abbreviation, L2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: L2
Context triple: [Ligue 2, abbreviation, L2]
  • 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. L2M
    L2M is a DARPA research initiative focused on developing AI systems capable of continuous, lifelong learning and adaptation.
  • C. 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.
  • D. 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.
  • E. L
    L is the vehicle registration code used on license plates for the German city and district of Leipzig.
  • 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: [Ligue 2, abbreviation, L2]
Generated description
L2 is the common shorthand for Ligue 2, the second tier of professional football in the French league system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: L2
Target entity description: L2 is the common shorthand for Ligue 2, the second tier of professional football in the French league system.
  • 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. L2M
    L2M is a DARPA research initiative focused on developing AI systems capable of continuous, lifelong learning and adaptation.
  • C. 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.
  • D. 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.
  • E. L
    L is the vehicle registration code used on license plates for the German city and district of Leipzig.
  • 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_69ca82aaaf24819084b94d18f699ba53 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3cf72fc08190aa78b97c1ab92f90 completed March 31, 2026, 3:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe127afe0819092d5ad0c430fadc4 completed March 31, 2026, 2:58 p.m.
NEDg Description generation batch_69cc46c221848190848c7e017e532a16 completed March 31, 2026, 10:12 p.m.
NED2 Entity disambiguation (via description) batch_69cc480d2f40819085046a1d0c9d05e0 completed March 31, 2026, 10:17 p.m.
Created at: March 30, 2026, 5:18 p.m.