Triple
T21132473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Rail Class 360 |
E520721
|
entity |
| Predicate | designedBy |
P184
|
FINISHED |
| Object | Siemens AG |
—
|
NE NERFINISHED |
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: Siemens AG | Statement: [British Rail Class 360, designedBy, Siemens AG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siemens AG Context triple: [British Rail Class 360, designedBy, Siemens AG]
-
A.
Siemens
chosen
Siemens is a major German multinational conglomerate best known for its leading roles in industrial manufacturing, energy, healthcare technology, and infrastructure solutions worldwide.
-
B.
S7 Group
S7 Group is a Russian aviation holding company best known for owning and operating S7 Airlines and related air transport businesses.
-
C.
Siemens–Duewag
Siemens–Duewag was a German rolling stock manufacturer known for producing light rail vehicles and trams used in many cities worldwide.
-
D.
Siemens Energy
Siemens Energy is a global energy technology company specializing in power generation, transmission, and related services for conventional and renewable energy systems.
-
E.
Dürr AG
Dürr AG is a German engineering company known globally for its production and automation technologies, particularly in painting, finishing, and environmental systems for the automotive and manufacturing industries.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b50b53048190ae34e8abbe3c5ada |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e723574b1481909fe9bebc83f1f3ce |
completed | April 21, 2026, 7:12 a.m. |
Created at: April 16, 2026, 2:56 p.m.