Triple
T10209375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MindSphere |
E242285
|
entity |
| Predicate | category |
P87
|
FINISHED |
| Object | Siemens digitalization portfolio |
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 digitalization portfolio | Statement: [MindSphere, category, Siemens digitalization portfolio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siemens digitalization portfolio Context triple: [MindSphere, category, Siemens digitalization portfolio]
-
A.
Siemens Nexas
Siemens Nexas is a class of electric multiple unit trains used for suburban passenger services on Melbourne’s metropolitan rail network.
-
B.
Siemens Avanto
Siemens Avanto is a family of light rail and tram-train vehicles developed by Siemens for urban and regional public transport systems.
-
C.
Siemens SD100
The Siemens SD100 is a light rail vehicle model built by Siemens for use on urban trolley and light rail systems such as the San Diego Trolley.
-
D.
Siemens S200
The Siemens S200 is a modern low-floor light rail vehicle used in North American transit systems, including Calgary’s CTrain, known for its improved accessibility, energy efficiency, and passenger comfort.
-
E.
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.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d395fa86cc8190b4f115b5a0f99772 |
completed | April 6, 2026, 11:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d652cca9c081909f705365c70db009 |
completed | April 8, 2026, 1:06 p.m. |
Created at: April 6, 2026, 11 a.m.