Triple

T2375973
Position Surface form Disambiguated ID Type / Status
Subject Texas's 3rd congressional district E46198 entity
Predicate containsCity P294 FINISHED
Object Anna
Anna is a small city in north-central Texas that forms part of the fast-growing suburban region north of Dallas.
 E301921 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: Anna | Statement: [Texas's 3rd congressional district, containsCity, Anna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Anna
Context triple: [Texas's 3rd congressional district, containsCity, Anna]
  • A. Anna
    Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • B. Anna
    Anna is the given name of Anna Murray Douglass, an African American abolitionist and the first wife of Frederick Douglass.
  • C. Anna
    Anna is a central female character in the comedy Western film "A Million Ways to Die in the West," portrayed as a sharp-shooting, quick-witted woman who helps the protagonist toughen up in the dangerous frontier.
  • D. Anna
    Anna is the given name of Anna Laetitia Barbauld, an influential 18th–19th century English poet, essayist, and children's author.
  • E. Anna
    Anna is traditionally revered in Christianity as the mother of the Virgin Mary and the grandmother of Jesus.
  • 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: Anna
Triple: [Texas's 3rd congressional district, containsCity, Anna]
Generated description
Anna is a small city in north-central Texas that forms part of the fast-growing suburban region north of Dallas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Anna
Target entity description: Anna is a small city in north-central Texas that forms part of the fast-growing suburban region north of Dallas.
  • A. Anna
    Anna is a central female character in the comedy Western film "A Million Ways to Die in the West," portrayed as a sharp-shooting, quick-witted woman who helps the protagonist toughen up in the dangerous frontier.
  • B. Anna
    Anna is a feminine given name of Hebrew origin meaning "grace" or "favor," widely used across many cultures and languages.
  • C. Anna
    Anna is traditionally revered in Christianity as the mother of the Virgin Mary and the grandmother of Jesus.
  • D. Anna
    Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • E. Anna
    Anna is the given name of pioneering Chinese American actress Anna May Wong, a trailblazing early Hollywood star and fashion icon.
  • 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_69a88a1554a48190a0180682bcf099be completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc794eee481908163148e1e666d9b completed March 7, 2026, 6:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69afce7a1c908190919280545ae4443c completed March 10, 2026, 7:55 a.m.
NEDg Description generation batch_69afcf9a54248190afcf80f1b66298b1 completed March 10, 2026, 8 a.m.
NED2 Entity disambiguation (via description) batch_69afd0bb64748190a3af9900a9add6fa completed March 10, 2026, 8:05 a.m.
Created at: March 4, 2026, 7:57 p.m.