Triple
T10923463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DASA |
E258003
|
entity |
| Predicate | formedByMergerOf |
P77
|
FINISHED |
| Object | Telefunken Systemtechnik |
E91046
|
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: Telefunken Systemtechnik | Statement: [DASA, formedByMergerOf, Telefunken Systemtechnik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Telefunken Systemtechnik Context triple: [DASA, formedByMergerOf, Telefunken Systemtechnik]
-
A.
Telefunken
chosen
Telefunken is a historic German electronics and television brand known for its radios, audio equipment, and consumer electronics.
-
B.
Erlecom
Erlecom is a small village in the Dutch province of Gelderland, situated along the Waal River within the municipality of Berg en Dal.
-
C.
Telesystem-Mesko
Telesystem-Mesko is a Polish defense company known for developing advanced guided missile and precision weapon systems.
-
D.
Fitel
Fitel was a financial technology startup where Jeff Bezos worked early in his career, before joining D. E. Shaw and later founding Amazon.
-
E.
Tandberg
Tandberg is a Norwegian company best known for its video conferencing and telepresence solutions, which became part of Cisco Systems after its acquisition.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7708e3fd881908da10f24a856364c |
completed | April 9, 2026, 9:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e2172894d88190b7b27f78e9fd1521 |
completed | April 17, 2026, 11:19 a.m. |
Created at: April 8, 2026, 9:22 p.m.