Triple
T2188283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemens |
E49800
|
entity |
| Predicate | productLine |
P3585
|
FINISHED |
| Object |
SINUMERIK
SINUMERIK is Siemens’ CNC automation system line used to control machine tools in manufacturing and industrial production.
|
E242283
|
NE FINISHED |
How this triple was built (4 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: SINUMERIK | Statement: [Siemens, productLine, SINUMERIK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SINUMERIK Context triple: [Siemens, productLine, SINUMERIK]
-
A.
Siemens S70
The Siemens S70 is a modern low-floor light rail vehicle widely used in North American urban transit systems.
-
B.
Siemens SD660
Siemens SD660 is a model of light rail vehicle built by Siemens for use in modern urban transit systems.
-
C.
Siemens NX
Siemens NX is a high-end computer-aided design, engineering, and manufacturing (CAD/CAM/CAE) software suite widely used for complex product development in industries such as automotive and aerospace.
-
D.
SINTRAN
SINTRAN is a real-time, multitasking operating system developed by Norsk Data for its NORD series of minicomputers.
-
E.
Siemens
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: SINUMERIK Triple: [Siemens, productLine, SINUMERIK]
Generated description
SINUMERIK is Siemens’ CNC automation system line used to control machine tools in manufacturing and industrial production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SINUMERIK Target entity description: SINUMERIK is Siemens’ CNC automation system line used to control machine tools in manufacturing and industrial production.
-
A.
Siemens S70
The Siemens S70 is a modern low-floor light rail vehicle widely used in North American urban transit systems.
-
B.
Siemens SD660
Siemens SD660 is a model of light rail vehicle built by Siemens for use in modern urban transit systems.
-
C.
Siemens NX
Siemens NX is a high-end computer-aided design, engineering, and manufacturing (CAD/CAM/CAE) software suite widely used for complex product development in industries such as automotive and aerospace.
-
D.
SINTRAN
SINTRAN is a real-time, multitasking operating system developed by Norsk Data for its NORD series of minicomputers.
-
E.
Siemens
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. chosen
Provenance (5 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_69a88aaba3c48190b351cab9b26989ff |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abbf373c608190b7716c137b3e9fe9 |
completed | March 7, 2026, 6:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5dada268819082ddc4acd58e19f3 |
completed | March 9, 2026, 5:42 a.m. |
| NEDg | Description generation | batch_69ae5e5fe37c8190bcf73200d32f5faa |
completed | March 9, 2026, 5:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae5ed1e3208190b46d5e8361c2a5f6 |
completed | March 9, 2026, 5:46 a.m. |
Created at: March 4, 2026, 7:45 p.m.