Triple

T3261408
Position Surface form Disambiguated ID Type / Status
Subject Teresa Capone E68418 entity
Predicate givenName P17 FINISHED
Object Teresa
Teresa is a feminine given name of Greek origin, commonly used in various cultures and languages.
 E64893 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 Capone, givenName, Teresa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Teresa
Context triple: [Teresa Capone, givenName, Teresa]
  • A. 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.
  • B. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • C. Santa Teresa Cora
    Santa Teresa Cora is a regional dialect of the Cora language spoken by the indigenous Cora people of western Mexico.
  • D. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • E. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • 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 Capone, givenName, Teresa]
Generated description
Teresa is a feminine given name of Greek origin, commonly used in various cultures and languages.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Teresa
Target entity description: Teresa is a feminine given name of Greek origin, commonly used in various cultures and languages.
  • A. Teresa chosen
    Teresa is the religious name of Mother Teresa, the Catholic nun and missionary renowned for her charitable work with the poor in Kolkata, India.
  • B. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • C. Santa Teresa Cora
    Santa Teresa Cora is a regional dialect of the Cora language spoken by the indigenous Cora people of western Mexico.
  • D. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • E. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • 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_69ad8590444081909e8107a8aeef3a23 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adafa7fea08190b089b6174fd7cd32 completed March 8, 2026, 5:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2e8369b708190aeddf21dd9440d6a completed March 12, 2026, 4:22 p.m.
NEDg Description generation batch_69b2e9c808188190b681557e010ce159 completed March 12, 2026, 4:28 p.m.
NED2 Entity disambiguation (via description) batch_69b2ea5d38808190a11ebcad2c384db7 completed March 12, 2026, 4:31 p.m.
Created at: March 8, 2026, 3:09 p.m.