Triple
T11095477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LOT Cargo |
E262365
|
entity |
| Predicate | hasMainOfficeAt |
P48357
|
FINISHED |
| Object | LOT Polish Airlines headquarters in Warsaw |
—
|
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: LOT Polish Airlines headquarters in Warsaw | Statement: [LOT Cargo, hasMainOfficeAt, LOT Polish Airlines headquarters in Warsaw]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainOfficeAt Context triple: [LOT Cargo, hasMainOfficeAt, LOT Polish Airlines headquarters in Warsaw]
-
A.
hasManufacturerHeadquartersIn
Indicates that the location specified is the place where the manufacturer’s main headquarters is situated.
-
B.
hasCorporateOffice
chosen
Indicates that an entity maintains a formal corporate office at a specified location or within another organizational entity.
-
C.
hasOperatorHeadquartersIn
Indicates that the primary operational headquarters of an operator is located in a specified place.
-
D.
hasHeadquartersType
Indicates the specific kind or classification of headquarters associated with an entity.
-
E.
employerHeadquarters
Indicates the location where an employer’s main corporate offices or central administrative operations are based.
- 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79a0897188190b6c293b44990b3d4 |
completed | April 9, 2026, 12:22 p.m. |
| PD | Predicate disambiguation | batch_69d7441aa3548190b92dbde57841c135 |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:27 p.m.