Triple

T11095342
Position Surface form Disambiguated ID Type / Status
Subject LOT Polish Airlines E262362 entity
Predicate callsign P1565 FINISHED
Object POLLOT E262363 NE 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: POLLOT | Statement: [LOT Polish Airlines, callsign, POLLOT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: POLLOT
Context triple: [LOT Polish Airlines, callsign, POLLOT]
  • A. POLLOT chosen
    POLLOT is the radio callsign used by LOT Polish Airlines for its flight operations.
  • B. Pato
    Pato is a Galician musician and educator best known internationally as a virtuoso gaita (Galician bagpipe) player and collaborator with jazz and classical ensembles.
  • C. Pato
    Pato is the stage name of Patrice Wilson, a Nigerian-American music producer and songwriter best known for creating viral pop songs such as Rebecca Black’s “Friday.”
  • D. Pato
    Pato is the nickname for the Talgo 350, a high-speed Spanish train known for its distinctive duck-bill-shaped nose and use on AVE services.
  • E. Polillo
    Polillo is a coastal island municipality in the province of Quezon, Philippines, known for its rich marine biodiversity and relatively remote, rural character.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d6aa9a40d88190a373e2c7e48285db completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d79a0897188190b6c293b44990b3d4 completed April 9, 2026, 12:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3e7e0c7e4819098e690ffebbd8e61 completed April 18, 2026, 8:21 p.m.
Created at: April 8, 2026, 9:27 p.m.