Triple
T6649509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allianz Stadium |
E150783
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Allianz |
E117565
|
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: Allianz | Statement: [Allianz Stadium, namedAfter, Allianz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allianz Context triple: [Allianz Stadium, namedAfter, Allianz]
-
A.
Allianz
chosen
Allianz is a leading global financial services company, best known as one of the world’s largest insurance and asset management providers.
-
B.
Swiss Re
Swiss Re is a leading global reinsurance company headquartered in Zurich, Switzerland, providing risk transfer and insurance solutions worldwide.
-
C.
Munich Re
Munich Re is a leading global reinsurance company based in Germany, known for providing risk management and insurance solutions worldwide.
-
D.
AIG
AIG (American International Group) is a global insurance and financial services corporation known for its extensive property-casualty, life insurance, and retirement products.
-
E.
AXA
AXA is a major French multinational insurance and asset management company headquartered in Paris.
- 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_69c687f2c9508190a60b9aad31d3f358 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b01fea4c8190a21ba7f4c2018c5e |
completed | March 27, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6f79642508190a2e3810e347f2e93 |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 2:01 p.m.