Triple
T2188258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemens |
E49800
|
entity |
| Predicate | tickerSymbol |
P1447
|
FINISHED |
| Object |
SIE
SIE is the stock ticker symbol for Siemens AG, a major German multinational conglomerate specializing in industrial manufacturing, energy, and infrastructure technologies.
|
E242277
|
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: SIE | Statement: [Siemens, tickerSymbol, SIE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SIE Context triple: [Siemens, tickerSymbol, SIE]
-
A.
SCE
SCE is the abbreviation for the Office of South Central European Affairs, a U.S. State Department office focused on diplomacy and policy in the South Central Europe region.
-
B.
EA
EA is the commonly used abbreviation for the Environment Agency, the public body responsible for environmental protection and regulation in England.
-
C.
Sony
Sony is a Japanese multinational conglomerate best known for its consumer electronics, gaming (PlayStation), entertainment, and imaging products.
-
D.
Sega
Sega is a Japanese video game and entertainment company best known for its iconic consoles and franchises such as Sonic the Hedgehog.
-
E.
IES
IES is the research, evaluation, and statistics arm of the U.S. Department of Education that provides rigorous evidence to inform education policy and practice.
- 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: SIE Triple: [Siemens, tickerSymbol, SIE]
Generated description
SIE is the stock ticker symbol for Siemens AG, a major German multinational conglomerate specializing in industrial manufacturing, energy, and infrastructure technologies.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SIE Target entity description: SIE is the stock ticker symbol for Siemens AG, a major German multinational conglomerate specializing in industrial manufacturing, energy, and infrastructure technologies.
-
A.
SCE
SCE is the abbreviation for the Office of South Central European Affairs, a U.S. State Department office focused on diplomacy and policy in the South Central Europe region.
-
B.
EA
EA is the commonly used abbreviation for the Environment Agency, the public body responsible for environmental protection and regulation in England.
-
C.
Sony
Sony is a Japanese multinational conglomerate best known for its consumer electronics, gaming (PlayStation), entertainment, and imaging products.
-
D.
Sega
Sega is a Japanese video game and entertainment company best known for its iconic consoles and franchises such as Sonic the Hedgehog.
-
E.
IES
IES is the research, evaluation, and statistics arm of the U.S. Department of Education that provides rigorous evidence to inform education policy and practice.
- 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.