Triple
T1892949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Concorde (Air France) |
E41912
|
entity |
| Predicate | cabinClassConfiguration |
P16894
|
FINISHED |
| Object | all-first-class layout |
—
|
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: all-first-class layout | Statement: [Concorde (Air France), cabinClassConfiguration, all-first-class layout]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cabinClassConfiguration Context triple: [Concorde (Air France), cabinClassConfiguration, all-first-class layout]
-
A.
cabinConfiguration
chosen
Indicates how the interior space of a vehicle, vessel, or aircraft is arranged and organized for occupants or cargo.
-
B.
hasCabinClass
Indicates that an entity (such as a booking, ticket, or seat) is associated with a specific cabin class (e.g., economy, business, first).
-
C.
hotelClass
Indicates the classification or rating level assigned to a hotel, such as its star category or quality tier.
-
D.
seatingConfiguration
Indicates how seats are arranged or organized relative to each other in a given context.
-
E.
hasCabins
Indicates that an entity possesses or includes one or more cabins as part of its structure or facilities.
- 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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1480a6c81909fcf5cce4c42fed4 |
completed | March 7, 2026, 5:02 a.m. |
| PD | Predicate disambiguation | batch_69abafe7e7e88190b58c0df59187c0c2 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:34 p.m.