Triple

T14743989
Position Surface form Disambiguated ID Type / Status
Subject Lev Praha E346418 entity
Predicate hasAbbreviation P43 FINISHED
Object LEV
LEV is the commonly used abbreviation for Lev Praha, a former professional ice hockey club based in Prague, Czech Republic.
E1116972 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: LEV | Statement: [Lev Praha, hasAbbreviation, LEV]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LEV
Context triple: [Lev Praha, hasAbbreviation, LEV]
  • A. LEV
    LEV is the vehicle registration code used on license plates for the German city of Leverkusen.
  • B. lev
    The lev is the official national currency of Bulgaria, used for everyday transactions and financial operations throughout the country.
  • C. LV
    LV is the two-letter ISO 3166-1 alpha-2 country code representing Latvia.
  • D. LV
    LV is an American rapper and singer best known for his soulful hook on Coolio’s Grammy-winning hit “Gangsta’s Paradise.”
  • E. LE
    LE is the abbreviation for Les Engagés, a centrist political party in Belgium that emerged from the reform of the former Humanist Democratic Centre (cdH).
  • 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: LEV
Triple: [Lev Praha, hasAbbreviation, LEV]
Generated description
LEV is the commonly used abbreviation for Lev Praha, a former professional ice hockey club based in Prague, Czech Republic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: LEV
Target entity description: LEV is the commonly used abbreviation for Lev Praha, a former professional ice hockey club based in Prague, Czech Republic.
  • A. LEV
    LEV is the vehicle registration code used on license plates for the German city of Leverkusen.
  • B. lev
    The lev is the official national currency of Bulgaria, used for everyday transactions and financial operations throughout the country.
  • C. LV
    LV is the two-letter ISO 3166-1 alpha-2 country code representing Latvia.
  • D. LV
    LV is an American rapper and singer best known for his soulful hook on Coolio’s Grammy-winning hit “Gangsta’s Paradise.”
  • E. LE
    LE is the abbreviation for Les Engagés, a centrist political party in Belgium that emerged from the reform of the former Humanist Democratic Centre (cdH).
  • 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_69d822e6f1c88190bc494d491a907114 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec7d002708190a32a4a45e96fc389 completed April 14, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdfb93e94c8190beba605e26d4552c completed May 8, 2026, 3:04 p.m.
NEDg Description generation batch_69fdff025e00819089ad734f74b64f26 completed May 8, 2026, 3:19 p.m.
NED2 Entity disambiguation (via description) batch_69fdff8bdefc8190a4a66c742e1cae83 completed May 8, 2026, 3:21 p.m.
Created at: April 10, 2026, 1:30 a.m.