Triple
T712152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemens S70 |
E14233
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object | Siemens AG |
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 AG | Statement: [Siemens S70, manufacturer, Siemens AG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siemens AG Context triple: [Siemens S70, manufacturer, 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.
Borsigwerke
Borsigwerke is a Berlin U-Bahn station on line U6 serving the Tegel district in the city’s northwest.
-
C.
General Electric
General Electric is a major American multinational conglomerate historically known for its leadership in industrial manufacturing, aviation, power, and healthcare technologies.
-
D.
Krupp (company)
Krupp (company) was a major German industrial conglomerate best known for its steel production and armaments manufacturing, playing a central role in both World Wars and in the development of heavy industry in Germany.
-
E.
Thales Group
Thales Group is a French multinational company specializing in aerospace, defense, security, and transportation technologies and systems.
- 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_69a4934a36e081909e7abef98b898a4e |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a55dd5908190bfb8816f65ea02e1 |
completed | March 1, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a63757e5848190b7c11820f67b20a7 |
completed | March 3, 2026, 1:20 a.m. |
Created at: March 1, 2026, 7:36 p.m.