Triple

T24050899
Position Surface form Disambiguated ID Type / Status
Subject Francis Marion University E595656 entity
Predicate hasFacility P105 FINISHED
Object Lee Nursing Building
The Lee Nursing Building is a dedicated healthcare education facility at Francis Marion University that houses its nursing programs, classrooms, and clinical training spaces.
 E1616345 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: Lee Nursing Building | Statement: [Francis Marion University, hasFacility, Lee Nursing Building]
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: Lee Nursing Building
Triple: [Francis Marion University, hasFacility, Lee Nursing Building]
Generated description
The Lee Nursing Building is a dedicated healthcare education facility at Francis Marion University that houses its nursing programs, classrooms, and clinical training spaces.

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_69e288c184b081909f1f1751fb8e299a completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1d9d1527c8190a4cd30bb1988a263 completed April 29, 2026, 10:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f96584554819092a0806b0e612516 completed May 21, 2026, 11:33 p.m.
NEDg Description generation batch_6a0f977b19808190909eeabe89e91e43 completed May 21, 2026, 11:38 p.m.
NED2 Entity disambiguation (via description) batch_6a0f98477c8881909af65b4bd2e22bb5 completed May 21, 2026, 11:41 p.m.
Created at: April 17, 2026, 10:20 p.m.