Triple

T31478723
Position Surface form Disambiguated ID Type / Status
Subject Le Cycle de la guerre des dieux E803073 entity
Predicate hasPart P35 FINISHED
Object Le Dieu des lumières
Le Dieu des lumières is a fantasy novel set in the universe of *Le Cycle de la guerre des dieux*, continuing its epic tale of divine conflicts and magical struggles.
E1965177 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: Le Dieu des lumières | Statement: [Le Cycle de la guerre des dieux, hasPart, Le Dieu des lumières]
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: Le Dieu des lumières
Triple: [Le Cycle de la guerre des dieux, hasPart, Le Dieu des lumières]
Generated description
Le Dieu des lumières is a fantasy novel set in the universe of *Le Cycle de la guerre des dieux*, continuing its epic tale of divine conflicts and magical struggles.

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_69f348c9477c8190bc0a21f6d482d2fc completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a1ac56b88190a820434b65c9fa23 completed May 3, 2026, 1:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b145a4c9c819087a29f04f80b7d91 completed June 11, 2026, 8:02 p.m.
NEDg Description generation batch_6a2b17844ab881909814fb8eff371951 completed June 11, 2026, 8:16 p.m.
NED2 Entity disambiguation (via description) batch_6a2b1d3082808190bbe7ac5a79b00439 completed June 11, 2026, 8:40 p.m.
Created at: April 30, 2026, 9:31 p.m.