Triple

T28390610
Position Surface form Disambiguated ID Type / Status
Subject L.V. Eberhard Center E719145 entity
Predicate namedAfter P63 FINISHED
Object L.V. Eberhard
L.V. Eberhard was a prominent figure associated with Grand Valley State University, honored for significant contributions that led to a major campus center bearing his name.
 E1817790 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: L.V. Eberhard | Statement: [L.V. Eberhard Center, namedAfter, L.V. Eberhard]
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: L.V. Eberhard
Triple: [L.V. Eberhard Center, namedAfter, L.V. Eberhard]
Generated description
L.V. Eberhard was a prominent figure associated with Grand Valley State University, honored for significant contributions that led to a major campus center bearing his name.

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_69eff6ef211081909d31d9be5f5567e6 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64cec950881909d89fd56511514a2 completed May 2, 2026, 7:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a163308547481909dcfcd7262347643 completed May 26, 2026, 11:55 p.m.
NEDg Description generation batch_6a163702f1bc8190b1331dfaa6b593dd completed May 27, 2026, 12:12 a.m.
NED2 Entity disambiguation (via description) batch_6a1637e4d0548190b8d4c7a90580032d completed May 27, 2026, 12:16 a.m.
Created at: April 28, 2026, 1:13 a.m.