Triple

T36144191
Position Surface form Disambiguated ID Type / Status
Subject Night Train to Lisbon E1045398 entity
Predicate mainCharacter P1183 FINISHED
Object Raimund Gregorius
Raimund Gregorius is a reserved Swiss classics teacher whose impulsive journey to Lisbon in the novel "Night Train to Lisbon" leads him into a profound exploration of identity, history, and moral choice.
E2172131 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: Raimund Gregorius | Statement: [Night Train to Lisbon, mainCharacter, Raimund Gregorius]
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: Raimund Gregorius
Triple: [Night Train to Lisbon, mainCharacter, Raimund Gregorius]
Generated description
Raimund Gregorius is a reserved Swiss classics teacher whose impulsive journey to Lisbon in the novel "Night Train to Lisbon" leads him into a profound exploration of identity, history, and moral choice.

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_69f76e37ace88190a906b107d388f5d1 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b33c88fc8190b17fd3d96be8a7f8 completed May 3, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a390d467b8c8190a52c03261a15e917 completed June 22, 2026, 10:24 a.m.
NEDg Description generation batch_6a390eaab52881909027bbc2cc2469ba completed June 22, 2026, 10:30 a.m.
NED2 Entity disambiguation (via description) batch_6a390f6f1ec88190b2fe251699996657 completed June 22, 2026, 10:33 a.m.
Created at: May 3, 2026, 4:08 p.m.