Triple

T32482577
Position Surface form Disambiguated ID Type / Status
Subject Reggie Nalder E830149 entity
Predicate birthName P65 FINISHED
Object Alfred Reginald Natzler
Alfred Reginald Natzler, better known by his stage name Reggie Nalder, was an Austrian character actor recognized for his distinctive, gaunt features and chilling performances in horror and thriller films.
E2019653 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: Alfred Reginald Natzler | Statement: [Reggie Nalder, birthName, Alfred Reginald Natzler]
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: Alfred Reginald Natzler
Triple: [Reggie Nalder, birthName, Alfred Reginald Natzler]
Generated description
Alfred Reginald Natzler, better known by his stage name Reggie Nalder, was an Austrian character actor recognized for his distinctive, gaunt features and chilling performances in horror and thriller films.

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_69f3491ff3b48190b50a7fa00bb05b1f completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c396f8b881909a940437d9c817ea completed May 3, 2026, 3:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a349e9faa60819098aca83794256981 completed June 19, 2026, 1:42 a.m.
NEDg Description generation batch_6a349f6592688190b12151bd9c3fe88a completed June 19, 2026, 1:46 a.m.
NED2 Entity disambiguation (via description) batch_6a34a1136c6881909ba05be90baba354 completed June 19, 2026, 1:53 a.m.
Created at: May 1, 2026, 12:58 a.m.