Triple
T18612187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans-Werner Hector |
E454921
|
entity |
| Predicate | coFounded |
P104
|
FINISHED |
| Object | SAP SE |
—
|
NE NERFINISHED |
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: [Hans-Werner Hector, coFounded, SAP SE]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAP SE Context triple: [Hans-Werner Hector, coFounded, SAP SE]
-
A.
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.
-
B.
SAP
SAP is the commonly used abbreviation for the Société d’Anthropologie de Paris, a French learned society dedicated to the study of anthropology.
-
C.
SAP
SAP is the station code for Lisbon Santa Apolónia, one of the main railway terminals in Lisbon, Portugal.
-
D.
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.
-
E.
SAP
SAP is the IATA airport code for Ramón Villeda Morales International Airport, the main air gateway serving San Pedro Sula, Honduras.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d38bbe7c8190bdec3138e7d413c9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54d01ebd08190b6e646e3a749d7f5 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 10, 2026, 11:45 a.m.