Triple

T36084283
Position Surface form Disambiguated ID Type / Status
Subject Energy Star for Buildings E1043733 entity
Predicate usesTool P98 FINISHED
Object ENERGY STAR Portfolio Manager
ENERGY STAR Portfolio Manager is an online tool that helps building owners and managers measure, track, and benchmark the energy and water performance of their properties.
E318638 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: ENERGY STAR Portfolio Manager | Statement: [Energy Star for Buildings, usesTool, ENERGY STAR Portfolio Manager]
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: ENERGY STAR Portfolio Manager
Triple: [Energy Star for Buildings, usesTool, ENERGY STAR Portfolio Manager]
Generated description
ENERGY STAR Portfolio Manager is an online tool that helps building owners and managers measure, track, and benchmark the energy and water performance of their properties.

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_69f76e3154908190a6f702671c2bea08 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b240cfa481908cc8b1b370f3c330 completed May 3, 2026, 8:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38d5428d64819099ebe4842364c767 completed June 22, 2026, 6:25 a.m.
NEDg Description generation batch_6a38d60d968081908071371e5bbc1314 completed June 22, 2026, 6:28 a.m.
NED2 Entity disambiguation (via description) batch_6a38d6b7722c81909093057618f2569a completed June 22, 2026, 6:31 a.m.
Created at: May 3, 2026, 4:08 p.m.