Triple

T7854532
Position Surface form Disambiguated ID Type / Status
Subject Annily Chatelain E182139 entity
Predicate givenName P17 FINISHED
Object Annily
Annily is a feminine given name, likely of French origin, used as a personal first name.
 E700063 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: Annily | Statement: [Annily Chatelain, givenName, Annily]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Annily
Context triple: [Annily Chatelain, givenName, Annily]
  • A. Neilia
    Neilia was an American educator best known as the first wife of Joe Biden, who tragically died in a car accident in 1972 along with their infant daughter.
  • B. Ylla
    Ylla is a short story by Ray Bradbury, set on Mars and exploring the inner life and unfulfilled desires of a Martian woman.
  • C. Norala
    Norala is a rural municipality in the province of South Cotabato in the Philippines, known for its agricultural economy and multicultural communities.
  • D. Dalva
    Dalva is a surname most notably associated with American film editor Robert Dalva, recognized for his work on major Hollywood productions.
  • E. Velda
    Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
  • 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: Annily
Triple: [Annily Chatelain, givenName, Annily]
Generated description
Annily is a feminine given name, likely of French origin, used as a personal first name.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Annily
Target entity description: Annily is a feminine given name, likely of French origin, used as a personal first name.
  • A. Neilia
    Neilia was an American educator best known as the first wife of Joe Biden, who tragically died in a car accident in 1972 along with their infant daughter.
  • B. Ylla
    Ylla is a short story by Ray Bradbury, set on Mars and exploring the inner life and unfulfilled desires of a Martian woman.
  • C. Norala
    Norala is a rural municipality in the province of South Cotabato in the Philippines, known for its agricultural economy and multicultural communities.
  • D. Dalva
    Dalva is a surname most notably associated with American film editor Robert Dalva, recognized for his work on major Hollywood productions.
  • E. Velda
    Velda is the loyal and resourceful secretary and love interest of private investigator Mike Hammer in the hardboiled crime novel and film "Kiss Me Deadly."
  • 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_69ca82869ee08190b8f9040dbc2c0467 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb1a72cfdc8190a3186c4c2894f571 completed March 31, 2026, 12:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b27e9b081909a0574458ddf43b0 completed March 31, 2026, 5:27 a.m.
NEDg Description generation batch_69cb762eab0881909c5035b3086dfdd9 completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbb801cc0c8190864d28e199eb5e67 completed March 31, 2026, 12:03 p.m.
Created at: March 30, 2026, 4:51 p.m.