Triple
T1647870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SAP |
E35622
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | SAP AG |
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 AG | Statement: [SAP, formerName, SAP AG]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SAP AG Context triple: [SAP, formerName, SAP AG]
-
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 was the former official currency of South Africa, used before the adoption of the South African rand.
-
C.
Siemens
Siemens is a major German multinational conglomerate best known for its leading roles in industrial manufacturing, energy, healthcare technology, and infrastructure solutions worldwide.
-
D.
Capgemini
Capgemini is a global consulting, technology services, and digital transformation company headquartered in France.
-
E.
HCL Technologies
HCL Technologies is a global Indian IT services and consulting company known for providing software development, infrastructure management, and digital transformation solutions to enterprises worldwide.
- 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a640ea88190822906da575d5165 |
completed | March 5, 2026, 4:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad681db3408190a3b469e319486419 |
completed | March 8, 2026, 12:14 p.m. |
Created at: March 4, 2026, 7:29 p.m.