Triple

T12973276
Position Surface form Disambiguated ID Type / Status
Subject AS Nancy E321456 entity
Predicate shortName P43 FINISHED
Object ASNL
ASNL is the commonly used abbreviation for AS Nancy Lorraine, a French professional football club based in Nancy.
 E1013603 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: ASNL | Statement: [AS Nancy, shortName, ASNL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ASNL
Context triple: [AS Nancy, shortName, ASNL]
  • A. ASSE
    ASSE is the commonly used abbreviation for AS Saint-Étienne, a historic French professional football club known for its success in Ligue 1.
  • B. ASUN
    ASUN is an NCAA Division I collegiate athletic conference primarily comprising universities in the southeastern United States.
  • C. ANLE
    ANLE is the North American Academy of the Spanish Language, an institution dedicated to studying, preserving, and promoting the correct use of Spanish in the United States.
  • D. ASLIA
    ASLIA is the professional association representing and supporting sign language interpreters in Australia.
  • E. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • 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: ASNL
Triple: [AS Nancy, shortName, ASNL]
Generated description
ASNL is the commonly used abbreviation for AS Nancy Lorraine, a French professional football club based in Nancy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ASNL
Target entity description: ASNL is the commonly used abbreviation for AS Nancy Lorraine, a French professional football club based in Nancy.
  • A. ASSE
    ASSE is the commonly used abbreviation for AS Saint-Étienne, a historic French professional football club known for its success in Ligue 1.
  • B. ASUN
    ASUN is an NCAA Division I collegiate athletic conference primarily comprising universities in the southeastern United States.
  • C. ANLE
    ANLE is the North American Academy of the Spanish Language, an institution dedicated to studying, preserving, and promoting the correct use of Spanish in the United States.
  • D. ASLIA
    ASLIA is the professional association representing and supporting sign language interpreters in Australia.
  • E. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • 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_69d80763bd6c819094437da5b20b01d2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e4322c08190abd43daadf16097f completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8ec821c81909398d8e02d69dcbf completed May 3, 2026, 2:54 a.m.
NEDg Description generation batch_69f6b9db8164819086a3a27692d681d5 completed May 3, 2026, 2:58 a.m.
NED2 Entity disambiguation (via description) batch_69f6bb03d8d88190930edd0f49fec8aa completed May 3, 2026, 3:03 a.m.
Created at: April 9, 2026, 8:36 p.m.