Triple
T2406813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LOT Polish Airlines |
E50294
|
entity |
| Predicate | IATAcode |
P418
|
FINISHED |
| Object | LO |
E175756
|
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: LO | Statement: [LOT Polish Airlines, IATAcode, LO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LO Context triple: [LOT Polish Airlines, IATAcode, LO]
-
A.
LO
LO is Norway’s largest and most influential trade union confederation, representing a broad spectrum of workers across multiple sectors.
-
B.
LO
chosen
LO was the New York Stock Exchange ticker symbol for Lorillard Tobacco Company, a major American tobacco manufacturer best known for brands like Newport.
-
C.
L
L is the vehicle registration code used on license plates for the German city and district of Leipzig.
-
D.
L
L is the enigmatic, emotionally complex protagonist of Hanne Ørstavik’s novel "Love," whose inner life and perspective drive the story’s exploration of isolation and longing.
-
E.
L
The L is a Chicago 'L' rapid transit line that serves the city’s West Side and western suburbs as part of the Chicago Transit Authority system.
- 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_69a88b0339a88190a1207333cd271cc9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc8fcd3008190b27325e6829ae7ce |
completed | March 7, 2026, 6:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3eba9d08190a2c63e590e08b4df |
completed | March 9, 2026, 11:50 a.m. |
Created at: March 4, 2026, 7:58 p.m.