Triple
T8093983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klaus Tschira |
E188936
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | SAP SE |
E35622
|
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: SAP SE | Statement: [Klaus Tschira, employer, SAP SE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAP SE Context triple: [Klaus Tschira, employer, SAP SE]
-
A.
SAP
SAP is the station code for Lisbon Santa Apolónia, one of the main railway terminals in Lisbon, Portugal.
-
B.
SAP
SAP is Sweden’s major center-left political party, historically associated with social democracy, the welfare state, and long periods of governing the country.
-
C.
SAP
SAP was the former official currency of South Africa, used before the adoption of the South African rand.
-
D.
SAP
chosen
SAP is a leading global enterprise software company best known for its ERP solutions that help organizations manage business operations and customer relations.
-
E.
SAP
SAP is the commonly used abbreviation for the Société d’Anthropologie de Paris, a French learned society dedicated to the study of anthropology.
- 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_69ca82b7b3e88190b9041ab0ef28b3cb |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb429089cc81909e4625f9cc7e305f |
completed | March 31, 2026, 3:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd3438ae2481909549a05f195cda39 |
completed | April 1, 2026, 3:05 p.m. |
Created at: March 30, 2026, 5:30 p.m.