Triple

T27608282
Position Surface form Disambiguated ID Type / Status
Subject Daniel Handler E700239 entity
Predicate hasWritten P2831 FINISHED
Object Watch Your Mouth
Watch Your Mouth is a darkly comic, experimental novel by Daniel Handler that blends family drama, mythic elements, and inventive narrative structure.
 E1780443 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: Watch Your Mouth | Statement: [Daniel Handler, hasWritten, Watch Your Mouth]
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: Watch Your Mouth
Triple: [Daniel Handler, hasWritten, Watch Your Mouth]
Generated description
Watch Your Mouth is a darkly comic, experimental novel by Daniel Handler that blends family drama, mythic elements, and inventive narrative structure.

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_69ef6a4e2e208190b63b7268f405785c completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f6309d85fc8190b1bd2af515c8ccc6 completed May 2, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12d0e99290819094b633869814075c completed May 24, 2026, 10:20 a.m.
NEDg Description generation batch_6a12d1a671948190add200d3ab2db641 completed May 24, 2026, 10:23 a.m.
NED2 Entity disambiguation (via description) batch_6a12d2747f6881909aa2a5b0c389a494 completed May 24, 2026, 10:27 a.m.
Created at: April 27, 2026, 2:10 p.m.