Triple
T15292393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VAL |
E365558
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object | Siemens |
E49800
|
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: Siemens | Statement: [VAL, manufacturer, Siemens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siemens Context triple: [VAL, manufacturer, Siemens]
-
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 Avanto
Siemens Avanto is a family of light rail and tram-train vehicles developed by Siemens for urban and regional public transport systems.
-
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.
Bosch
Bosch is a multinational engineering and technology company best known for its automotive components, industrial products, and household appliances.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03680b60c8190a3ea54a9d34c8105 |
completed | April 16, 2026, 1:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef899e55c8190b1c26491bf37967a |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:15 a.m.