Triple

T31339994
Position Surface form Disambiguated ID Type / Status
Subject John L. Miller Great Neck North High School E799279 entity
Predicate namedAfter P63 FINISHED
Object John L. Miller
John L. Miller was a prominent figure in the Great Neck, New York community whose contributions to local education led to a high school being named in his honor.
 E1981117 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: John L. Miller | Statement: [John L. Miller Great Neck North High School, namedAfter, John L. Miller]
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: John L. Miller
Triple: [John L. Miller Great Neck North High School, namedAfter, John L. Miller]
Generated description
John L. Miller was a prominent figure in the Great Neck, New York community whose contributions to local education led to a high school being named in his honor.

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_69f224e3f6ac8190a13488516abca7c9 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69f127fdc81908f3493146be59cef completed May 3, 2026, 1:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e657e5c208190ae01dcfd2202db0a completed June 14, 2026, 8:25 a.m.
NEDg Description generation batch_6a2e76780fd88190a466ea2fb02a8eef completed June 14, 2026, 9:38 a.m.
NED2 Entity disambiguation (via description) batch_6a2e799fcc788190b81781d09914243e completed June 14, 2026, 9:51 a.m.
Created at: April 29, 2026, 9:16 p.m.