Triple

T24269503
Position Surface form Disambiguated ID Type / Status
Subject S&P Global E605237 entity
Predicate owns P347 FINISHED
Object S&P Global Engineering Solutions
S&P Global Engineering Solutions is a business unit of S&P Global that provides specialized engineering data, software, and analytics to support design, compliance, and operational decision-making across industrial and energy sectors.
E605237 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: S&P Global Engineering Solutions | Statement: [S&P Global, owns, S&P Global Engineering Solutions]
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: S&P Global Engineering Solutions
Triple: [S&P Global, owns, S&P Global Engineering Solutions]
Generated description
S&P Global Engineering Solutions is a business unit of S&P Global that provides specialized engineering data, software, and analytics to support design, compliance, and operational decision-making across industrial and energy sectors.

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_69e2954707dc8190915551eb114cfff6 completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f28d58666881909f28f4d4f0f7d590 completed April 29, 2026, 10:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9befef48190acd94667fc4c0f71 completed May 22, 2026, 3:13 a.m.
NEDg Description generation batch_6a0fcb28386881909ee80082449cf249 completed May 22, 2026, 3:19 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcbe6adc8819094c7d659d5ee63e3 completed May 22, 2026, 3:22 a.m.
Created at: April 18, 2026, 12:07 a.m.