Triple

T21449105
Position Surface form Disambiguated ID Type / Status
Subject Ahney Her E529159 entity
Predicate familyName P18 FINISHED
Object Her
Her is a surname of Hmong origin borne by various individuals, including American actress Ahney Her.
 E1485553 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: Her | Statement: [Ahney Her, familyName, Her]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Her
Context triple: [Ahney Her, familyName, Her]
  • A. Her
    Her is the standard three-letter IAU abbreviation for the northern constellation Hercules.
  • B. Her
    Her is a 2013 science-fiction romantic drama film directed by Spike Jonze that explores a man's emotional relationship with an advanced artificial intelligence operating system.
  • C. Her
    "Her" is a lesser-known work by American poet, painter, and City Lights Books co-founder Lawrence Ferlinghetti, reflecting his characteristic Beat-influenced, avant-garde literary style.
  • D. Her
    "Her" is a soulful R&B song by American singer-songwriter SiR, known for its smooth production and introspective lyrics about love and vulnerability.
  • E. HER
    HER is a reinforcement learning technique that improves learning from sparse rewards by reinterpreting failed experiences as successful ones for alternative goals.
  • 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: Her
Triple: [Ahney Her, familyName, Her]
Generated description
Her is a surname of Hmong origin borne by various individuals, including American actress Ahney Her.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Her
Target entity description: Her is a surname of Hmong origin borne by various individuals, including American actress Ahney Her.
  • A. Her
    Her is a 2013 science-fiction romantic drama film directed by Spike Jonze that explores a man's emotional relationship with an advanced artificial intelligence operating system.
  • B. Her
    "Her" is a lesser-known work by American poet, painter, and City Lights Books co-founder Lawrence Ferlinghetti, reflecting his characteristic Beat-influenced, avant-garde literary style.
  • C. Her
    "Her" is a soulful R&B song by American singer-songwriter SiR, known for its smooth production and introspective lyrics about love and vulnerability.
  • D. Her
    Her is the standard three-letter IAU abbreviation for the northern constellation Hercules.
  • E. HER
    HER is a reinforcement learning technique that improves learning from sparse rewards by reinterpreting failed experiences as successful ones for alternative goals.
  • 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_69e0c457579481909db68053ed99750c completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e9e9d11ca48190aafe25c97dfa5578 completed April 23, 2026, 9:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09c907cb9c8190b4a50551b274a915 completed May 17, 2026, 1:56 p.m.
NEDg Description generation batch_6a09ca21f52481909aa6033dbe4c5985 completed May 17, 2026, 2:01 p.m.
NED2 Entity disambiguation (via description) batch_6a09cb9587a88190b0e1c0d25d3733da completed May 17, 2026, 2:07 p.m.
Created at: April 16, 2026, 6:06 p.m.