Triple

T25704799
Position Surface form Disambiguated ID Type / Status
Subject The Mourning Bride E644556 entity
Predicate quoteParaphrasedAs P145832 FINISHED
Object "Hell hath no fury like a woman scorned"
"Hell hath no fury like a woman scorned" is a famous proverb expressing the intense anger and vengefulness of a woman who has been rejected or betrayed in love.
 E1691838 NE FINISHED

How this triple was built (3 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: "Hell hath no fury like a woman scorned" | Statement: [The Mourning Bride, quoteParaphrasedAs, "Hell hath no fury like a woman scorned"]
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: "Hell hath no fury like a woman scorned"
Triple: [The Mourning Bride, quoteParaphrasedAs, "Hell hath no fury like a woman scorned"]
Generated description
"Hell hath no fury like a woman scorned" is a famous proverb expressing the intense anger and vengefulness of a woman who has been rejected or betrayed in love.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: quoteParaphrasedAs
Context triple: [The Mourning Bride, quoteParaphrasedAs, "Hell hath no fury like a woman scorned"]
  • A. quoteType
    Indicates the specific category or classification of a quotation, such as its style, purpose, or contextual role in discourse.
  • B. quoteLanguage
    Indicates that a quoted text is expressed in a particular language.
  • C. quoteProvision
    Indicates that one entity supplies or presents a quotation or price estimate to another entity.
  • D. quotationText
    Indicates that the associated text is the exact content of a quotation made or referenced in the relationship.
  • E. quotedWork chosen
    Indicates that one entity is a work (e.g., text, speech, or media) that is quoted or cited within another entity.
  • F. None of above.

Provenance (6 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_69e77e83c8ec8190bf52fcdac4838984 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fc1052508190bee01272205a9f70 completed May 2, 2026, 1:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10c16eac0481908cc868d18d39b123 completed May 22, 2026, 8:49 p.m.
NEDg Description generation batch_6a10c3e6d8ac81908a6e9f2bde52e91b completed May 22, 2026, 9 p.m.
NED2 Entity disambiguation (via description) batch_6a10c459a0688190a40ab9a407769140 completed May 22, 2026, 9:02 p.m.
PD Predicate disambiguation batch_69f480824a1c81908a8a492eedbc2596 completed May 1, 2026, 10:29 a.m.
Created at: April 21, 2026, 9:02 p.m.