Triple

T23599669
Position Surface form Disambiguated ID Type / Status
Subject Apollo 11 mission team E582717 entity
Predicate hasMember P10 FINISHED
Object Glynn Lunney
Glynn Lunney was a NASA flight director and aerospace engineer renowned for his pivotal leadership in Mission Control during the Apollo program, including the Apollo 11 Moon landing.
E1639349 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: Glynn Lunney | Statement: [Apollo 11 mission team, hasMember, Glynn Lunney]
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: Glynn Lunney
Triple: [Apollo 11 mission team, hasMember, Glynn Lunney]
Generated description
Glynn Lunney was a NASA flight director and aerospace engineer renowned for his pivotal leadership in Mission Control during the Apollo program, including the Apollo 11 Moon landing.

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_69e248faa2788190abb1581742daa6aa completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1b0929c548190bb42621d1131d87f completed April 29, 2026, 7:17 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fee41e8a48190983812061b15596d completed May 22, 2026, 5:48 a.m.
NEDg Description generation batch_6a0fefb19fa881909157ec86c395b682 completed May 22, 2026, 5:54 a.m.
NED2 Entity disambiguation (via description) batch_6a0ff0cc90508190b5d68bedeb4531aa completed May 22, 2026, 5:59 a.m.
Created at: April 17, 2026, 6:43 p.m.