Triple

T32874491
Position Surface form Disambiguated ID Type / Status
Subject Le Docteur Lerne, sous-dieu E840886 entity
Predicate hasTitleTranslation P2303 FINISHED
Object Doctor Lerne, Under-God
Doctor Lerne, Under-God is an English translation of Maurice Renard’s early 20th-century French science fiction novel about a mad scientist whose godlike experiments blur the boundaries between humans, animals, and machines.
E2025244 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: Doctor Lerne, Under-God | Statement: [Le Docteur Lerne, sous-dieu, hasTitleTranslation, Doctor Lerne, Under-God]
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: Doctor Lerne, Under-God
Triple: [Le Docteur Lerne, sous-dieu, hasTitleTranslation, Doctor Lerne, Under-God]
Generated description
Doctor Lerne, Under-God is an English translation of Maurice Renard’s early 20th-century French science fiction novel about a mad scientist whose godlike experiments blur the boundaries between humans, animals, and machines.

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_69f349436ee88190b72ee12d0f3f508e completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6cfea53a08190a0e9de4d07330eb8 completed May 3, 2026, 4:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34bd1909ac81908dfc7c4c217fa6b4 completed June 19, 2026, 3:52 a.m.
NEDg Description generation batch_6a34bdc44d38819096c9e903b08e6ebd completed June 19, 2026, 3:55 a.m.
NED2 Entity disambiguation (via description) batch_6a34be47ee3c81909adac4069e76e8c4 completed June 19, 2026, 3:58 a.m.
Created at: May 1, 2026, 1:18 a.m.