Triple

T36265276
Position Surface form Disambiguated ID Type / Status
Subject Kissing the Witch E892202 entity
Predicate notableStory P7331 FINISHED
Object The Tale of the Kiss
The Tale of the Kiss is a short story from Emma Donoghue’s collection *Kissing the Witch*, which reimagines a classic fairy tale through a feminist, queer, and psychologically nuanced lens.
E2175992 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: The Tale of the Kiss | Statement: [Kissing the Witch, notableStory, The Tale of the Kiss]
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: The Tale of the Kiss
Triple: [Kissing the Witch, notableStory, The Tale of the Kiss]
Generated description
The Tale of the Kiss is a short story from Emma Donoghue’s collection *Kissing the Witch*, which reimagines a classic fairy tale through a feminist, queer, and psychologically nuanced lens.

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_69f76e4699188190af045b11a840ce31 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b625b090819095a9db9382211af7 completed May 3, 2026, 8:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a396e0c50508190953c4b30f6dc227e completed June 22, 2026, 5:17 p.m.
NEDg Description generation batch_6a396ea572248190986c4f71d6cde887 completed June 22, 2026, 5:19 p.m.
NED2 Entity disambiguation (via description) batch_6a396f3ec8bc8190b9f3a25381b8a52d completed June 22, 2026, 5:22 p.m.
Created at: May 3, 2026, 4:09 p.m.