Triple
T2082146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Future World |
E45266
|
entity |
| Predicate | sponsor |
P67
|
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: [Future World, sponsor, Siemens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siemens Context triple: [Future World, sponsor, 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.
Bosch
Bosch is a multinational engineering and technology company best known for its automotive components, industrial products, and household appliances.
-
D.
Schneider Electric
Schneider Electric is a French multinational company specializing in energy management and industrial automation solutions for homes, buildings, data centers, infrastructure, and industry.
-
E.
General Electric
General Electric is a major American multinational conglomerate historically known for its leadership in industrial manufacturing, aviation, power, and healthcare technologies.
- 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_69a8891869c88190a02643e3bb746f59 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abba35c2588190933dba882f52dd17 |
completed | March 7, 2026, 5:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae273ad87c8190a7b1407868692202 |
completed | March 9, 2026, 1:49 a.m. |
Created at: March 4, 2026, 7:41 p.m.