Triple
T649539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonardo S.p.A. |
E11314
|
entity |
| Predicate | collaboratesWith |
P37
|
FINISHED |
| Object | Airbus |
E6021
|
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: Airbus | Statement: [Leonardo S.p.A., collaboratesWith, Airbus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Airbus Context triple: [Leonardo S.p.A., collaboratesWith, Airbus]
-
A.
Airbus
chosen
Airbus is a major European aerospace corporation known for designing and manufacturing commercial airliners such as the A320, A330, and A380 families.
-
B.
Aérospatiale
Aérospatiale was a major French aerospace manufacturer and state-owned company that played a key role in European aviation and space projects, including as a founding partner of Airbus.
-
C.
Dassault Aviation
Dassault Aviation is a French aerospace company renowned for designing and producing military fighter jets and business aircraft, including the Mirage and Rafale families and Falcon business jets.
-
D.
Boeing
Boeing is a major American aerospace company best known for designing and manufacturing commercial jetliners and military aircraft used worldwide.
-
E.
Embraer
Embraer is a Brazilian aerospace company best known globally for designing and manufacturing regional and business jets used by airlines and operators worldwide.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f31e70c81909a2ac1d939f7ec07 |
completed | March 1, 2026, 8:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a58a716de88190854e13fb5143b9b3 |
completed | March 2, 2026, 1:02 p.m. |
Created at: March 1, 2026, 7:36 p.m.