Triple

T27637358
Position Surface form Disambiguated ID Type / Status
Subject SAP HANA E696503 entity
Predicate runsOn P23 FINISHED
Object SAP Business Technology Platform
SAP Business Technology Platform is SAP’s integrated cloud-based foundation for developing, extending, and running business applications and data solutions using technologies like databases, analytics, integration, and AI.
E1784764 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 Business Technology Platform | Statement: [SAP HANA, runsOn, SAP Business Technology Platform]
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: SAP Business Technology Platform
Triple: [SAP HANA, runsOn, SAP Business Technology Platform]
Generated description
SAP Business Technology Platform is SAP’s integrated cloud-based foundation for developing, extending, and running business applications and data solutions using technologies like databases, analytics, integration, and AI.

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_69ef5909f3848190805f35b76833e722 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f6318d4f2c81908fe25c3dd5d68e94 completed May 2, 2026, 5:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12da8ab2248190980616df9a4e3266 completed May 24, 2026, 11:01 a.m.
NEDg Description generation batch_6a12db0cb9648190b394ef4009fda2b4 completed May 24, 2026, 11:03 a.m.
NED2 Entity disambiguation (via description) batch_6a12dbf956748190a6763112e384f761 completed May 24, 2026, 11:07 a.m.
Created at: April 27, 2026, 2:24 p.m.