Triple

T11095341
Position Surface form Disambiguated ID Type / Status
Subject LOT Polish Airlines E262362 entity
Predicate ICAOcode P419 FINISHED
Object LOT E262362 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: LOT | Statement: [LOT Polish Airlines, ICAOcode, LOT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LOT
Context triple: [LOT Polish Airlines, ICAOcode, LOT]
  • A. LOT chosen
    LOT is the national flag carrier airline of Poland, headquartered in Warsaw and operating an extensive network of domestic and international flights.
  • B. Lot
    Lot is a department in southwestern France known for its picturesque river valleys, medieval villages, and prehistoric cave art.
  • C. Lot
    Lot is a river in southwestern France known for flowing through scenic valleys and historic towns before joining the Garonne.
  • D. Lot
    Lot is a prophet in the Abrahamic tradition known for preaching against the immoral practices of his people and for the divine destruction of the cities of Sodom and Gomorrah.
  • E. LOTS
    LOTS is the stock ticker symbol for Lotus Development Corporation, a pioneering software company best known for its Lotus 1-2-3 spreadsheet application.
  • 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.