Triple

T1563802
Position Surface form Disambiguated ID Type / Status
Subject Alitalia E33385 entity
Predicate hadFinancialProblems P24789 FINISHED
Object chronic losses LITERAL FINISHED

How this triple was built (2 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: chronic losses | Statement: [Alitalia, hadFinancialProblems, chronic losses]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hadFinancialProblems
Context triple: [Alitalia, hadFinancialProblems, chronic losses]
  • A. hasEconomicChallenge chosen
    Indicates that an entity is experiencing or facing a financial or economic difficulty, constraint, or problem.
  • B. filedForBankruptcy
    Indicates that an entity has formally initiated legal proceedings to declare inability to pay its debts and seek protection or reorganization under bankruptcy law.
  • C. hasTragicPast
    Indicates that an entity has experienced a significantly sorrowful or traumatic history that influences its present state or characterization.
  • D. hasLessFinancialPowersThan
    Indicates that one entity’s authority or capacity to make financial decisions or control financial resources is lower or more limited than that of another entity.
  • E. largestDeficitOvercome
    Indicates the maximum deficit or point difference that was successfully overcome by one side to achieve a comeback in a contest or competition.
  • F. None of above.

Provenance (3 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_69a885ef9cf48190b0af0f5ce3d02231 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90fccd4b48190a44012888a00af7f completed March 5, 2026, 5:08 a.m.
PD Predicate disambiguation batch_69a907b872f0819096b3df6ad502c63e completed March 5, 2026, 4:34 a.m.
Created at: March 4, 2026, 7:27 p.m.