Triple

T29689403
Position Surface form Disambiguated ID Type / Status
Subject Feng Jicai E751173 entity
Predicate notableWork P4 FINISHED
Object Ten Years of Madness
Ten Years of Madness is a renowned collection of short stories by Chinese writer Feng Jicai that vividly depicts the human suffering and social upheaval of the Cultural Revolution.
 E1879127 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: Ten Years of Madness | Statement: [Feng Jicai, notableWork, Ten Years of Madness]
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: Ten Years of Madness
Triple: [Feng Jicai, notableWork, Ten Years of Madness]
Generated description
Ten Years of Madness is a renowned collection of short stories by Chinese writer Feng Jicai that vividly depicts the human suffering and social upheaval of the Cultural Revolution.

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_69f0d625b09481909b0b69aea1e846c8 completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f67292c75c8190a09ab2fb88fc1a33 completed May 2, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_6a267eca95c481909b7fdf59e5524762 completed June 8, 2026, 8:35 a.m.
NEDg Description generation batch_6a2682e65ae08190a4d7e0ba5b6cf7f1 completed June 8, 2026, 8:52 a.m.
NED2 Entity disambiguation (via description) batch_6a2687743cfc81909c3a7679bfb29c35 completed June 8, 2026, 9:12 a.m.
Created at: April 28, 2026, 7:15 p.m.