Triple
T4421664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TO |
E95110
|
entity |
| Predicate | airlineOwnershipStructure |
P41157
|
FINISHED |
| Object | subsidiary of Air France-KLM group |
—
|
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: subsidiary of Air France-KLM group | Statement: [TO, airlineOwnershipStructure, subsidiary of Air France-KLM group]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airlineOwnershipStructure Context triple: [TO, airlineOwnershipStructure, subsidiary of Air France-KLM group]
-
A.
airlineParentCompany
chosen
Indicates that one company is the parent or owning company of an airline.
-
B.
associatedAirlineHeadquarters
Indicates that an airline is connected to or based at a particular headquarters location.
-
C.
airlineType
Indicates the classification or category of an airline based on its operational or service characteristics.
-
D.
ownsAirline
Indicates that one entity has legal ownership or controlling interest in an airline company.
-
E.
hasAirlines
Indicates that one entity (such as an airport, city, or country) is served by or associated with one or more airline operators.
- 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_69b3453a36908190b95a79a297ca083c |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3554a0e7c8190b704d00d07b1857d |
completed | March 13, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69b34f5eabe88190a12b244ea71e46d6 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:30 p.m.