Triple

T22263324
Position Surface form Disambiguated ID Type / Status
Subject Teresa Weatherspoon E550284 entity
Predicate givenName P17 FINISHED
Object Teresa
Teresa is a feminine given name commonly used in various cultures, often associated with notable religious and historical figures.
 E553842 NE FINISHED

How this triple was built (4 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: Teresa | Statement: [Teresa Weatherspoon, givenName, Teresa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Teresa
Context triple: [Teresa Weatherspoon, givenName, Teresa]
  • A. Teresa
    Teresa is the central protagonist of the play "The Memory of Water," around whom the story’s emotional and familial conflicts revolve.
  • B. Teresa
    Teresa is a fictional character from the animated television series "Cul-de-sac."
  • C. Teresa
    Teresa is a Mexican telenovela that helped launch Salma Hayek to fame through her lead role as an ambitious, morally conflicted young woman.
  • D. Teresa
    Teresa is a central figure in Carlos Fuentes’s novel "The Death of Artemio Cruz," representing both a pivotal love interest and a symbol of the social and emotional conflicts surrounding the protagonist.
  • E. Teresa
    Teresa is the middle name of Tamar Teresa Day Hennessy.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Teresa
Triple: [Teresa Weatherspoon, givenName, Teresa]
Generated description
Teresa is a feminine given name commonly used in various cultures, often associated with notable religious and historical figures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Teresa
Target entity description: Teresa is a feminine given name commonly used in various cultures, often associated with notable religious and historical figures.
  • A. Teresa chosen
    Teresa is a feminine given name commonly used in various cultures, often associated with notable religious and historical figures.
  • B. Teresa
    Teresa is the religious name of Mother Teresa, the Catholic nun and missionary renowned for her charitable work with the poor in Kolkata, India.
  • C. Teresa
    Teresa is the middle name of Tamar Teresa Day Hennessy.
  • D. Teresa
    Teresa is a central figure in Carlos Fuentes’s novel "The Death of Artemio Cruz," representing both a pivotal love interest and a symbol of the social and emotional conflicts surrounding the protagonist.
  • E. Teresa
    Teresa is a Mexican telenovela that helped launch Salma Hayek to fame through her lead role as an ambitious, morally conflicted young woman.
  • F. None of above.

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_69e11e42adb8819087714772ea606709 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f141b94a688190b17c55477993a745 completed April 28, 2026, 11:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0ab66c7f248190be0b13342f843eb8 completed May 18, 2026, 6:49 a.m.
NEDg Description generation batch_6a0ab7b53aa881908227842536d7e8cc completed May 18, 2026, 6:54 a.m.
NED2 Entity disambiguation (via description) batch_6a0ab82b5a988190a55dd87f10664821 completed May 18, 2026, 6:56 a.m.
Created at: April 16, 2026, 8:39 p.m.