Triple

T2052684
Position Surface form Disambiguated ID Type / Status
Subject Taryn Power E45603 entity
Predicate notableWork P4 FINISHED
Object María
"María" is a film featuring actress Taryn Power in a significant role.
 E238672 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: María | Statement: [Taryn Power, notableWork, María]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: María
Context triple: [Taryn Power, notableWork, María]
  • A. 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.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • D. Josefa
    Josefa is a feminine given name of Spanish origin, historically borne by notable figures such as Mexican independence heroine Josefa Ortiz de Domínguez.
  • E. Pilar
    Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
  • 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: María
Triple: [Taryn Power, notableWork, María]
Generated description
"María" is a film featuring actress Taryn Power in a significant role.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: María
Target entity description: "María" is a film featuring actress Taryn Power in a significant role.
  • A. 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.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • D. Josefa
    Josefa is a feminine given name of Spanish origin, historically borne by notable figures such as Mexican independence heroine Josefa Ortiz de Domínguez.
  • E. Pilar
    Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
  • F. None of above. chosen

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_69a8891a19508190a12ef1e192308dcb completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb99078d88190bc30fa4596f0bae8 completed March 7, 2026, 5:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae58c2cb688190ae3320bbf4e0dfa9 completed March 9, 2026, 5:21 a.m.
NEDg Description generation batch_69ae59b4f8188190bd0a09205233603c completed March 9, 2026, 5:25 a.m.
NED2 Entity disambiguation (via description) batch_69ae5a3079b08190ba25a44d4041f687 completed March 9, 2026, 5:27 a.m.
Created at: March 4, 2026, 7:39 p.m.