Triple
T5156658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Messerschmitt-Bölkow-Blohm |
E116326
|
entity |
| Predicate | becamePartOf |
P960
|
FINISHED |
| Object | DASA |
E258003
|
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: DASA | Statement: [Messerschmitt-Bölkow-Blohm, becamePartOf, DASA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DASA Context triple: [Messerschmitt-Bölkow-Blohm, becamePartOf, DASA]
-
A.
DASA
chosen
DASA (Deutsche Aerospace AG) was a major German aerospace and defense company that became a core component of the later European aerospace giant Airbus Group.
-
B.
DAS
DAS is the acronym for the Defense Attache Service, the U.S. military organization that manages defense attachés and military diplomatic representation at American embassies worldwide.
-
C.
DASC
DASC is a leading annual technical conference focused on digital avionics systems, organized jointly by IEEE and AIAA.
-
D.
DAV
DAV is the station code used to identify Davisville station in the Toronto subway system.
-
E.
ADESA
ADESA is a major North American vehicle auction and remarketing company that provides wholesale used-vehicle auctions and related services to automotive dealers, manufacturers, and fleet operators.
- 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_69bd445d94788190b72e2cc563120995 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd79019c6481909641f173c5b3769a |
completed | March 20, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed927ad5481909907c8a1764e9fd8 |
completed | March 21, 2026, 5:45 p.m. |
Created at: March 20, 2026, 1:44 p.m.