Triple

T27155871
Position Surface form Disambiguated ID Type / Status
Subject University Park campus E682516 entity
Predicate hasAthleticFacility P33887 FINISHED
Object Jeffrey Field
Jeffrey Field is a soccer stadium at Penn State University's University Park campus, serving as the home venue for the Nittany Lions men's and women's soccer teams.
E1759934 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: Jeffrey Field | Statement: [University Park campus, hasAthleticFacility, Jeffrey Field]
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: Jeffrey Field
Triple: [University Park campus, hasAthleticFacility, Jeffrey Field]
Generated description
Jeffrey Field is a soccer stadium at Penn State University's University Park campus, serving as the home venue for the Nittany Lions men's and women's soccer teams.

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_69eefaceb2a08190b9659b7f730629f5 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f625069a00819096cf4b71a69a3563 completed May 2, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12537e83e4819096c87709bdc99310 completed May 24, 2026, 1:25 a.m.
NEDg Description generation batch_6a12556ad52c8190a5f3549c8ce6bdc4 completed May 24, 2026, 1:33 a.m.
NED2 Entity disambiguation (via description) batch_6a1255ae6dc4819080c51cc112f2fd82 completed May 24, 2026, 1:34 a.m.
Created at: April 27, 2026, 9:16 a.m.