Triple

T1493194
Position Surface form Disambiguated ID Type / Status
Subject Annette Chaplin E29627 entity
Predicate givenName P17 FINISHED
Object Annette
Annette is a feminine given name of French origin, commonly used in various European and English-speaking countries.
 E170799 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: Annette | Statement: [Annette Chaplin, givenName, Annette]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Annette
Context triple: [Annette Chaplin, givenName, Annette]
  • A. Gigi
    Gigi is a 1958 American musical romantic comedy film, directed by Vincente Minnelli, that won multiple Academy Awards and is celebrated for its lavish production and memorable score.
  • B. Starlette
    Starlette is a lightweight, high-performance ASGI framework for building asynchronous web applications and services in Python.
  • C. Songsong
    Songsong is the main village and administrative center of the island municipality of Rota in the Northern Mariana Islands.
  • D. Julie
    Julie is a feminine given name of Latin origin, commonly used in many Western countries.
  • E. Lulu
    Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
  • 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: Annette
Triple: [Annette Chaplin, givenName, Annette]
Generated description
Annette is a feminine given name of French origin, commonly used in various European and English-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Annette
Target entity description: Annette is a feminine given name of French origin, commonly used in various European and English-speaking countries.
  • A. Gigi
    Gigi is a 1958 American musical romantic comedy film, directed by Vincente Minnelli, that won multiple Academy Awards and is celebrated for its lavish production and memorable score.
  • B. Starlette
    Starlette is a lightweight, high-performance ASGI framework for building asynchronous web applications and services in Python.
  • C. Songsong
    Songsong is the main village and administrative center of the island municipality of Rota in the Northern Mariana Islands.
  • D. Julie
    Julie is a feminine given name of Latin origin, commonly used in many Western countries.
  • E. Lulu
    Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
  • 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_69a498dba1d8819093b46a3a8d2485f1 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c6c665488190ae665f7a1b0563f5 completed March 1, 2026, 11:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad1cabe25c8190ba1d285a210a00f0 completed March 8, 2026, 6:52 a.m.
NEDg Description generation batch_69ad1fb7e4448190a7bca159cd5a4be7 completed March 8, 2026, 7:05 a.m.
NED2 Entity disambiguation (via description) batch_69ad2014a0c481908f2f66fc742fa90f completed March 8, 2026, 7:07 a.m.
Created at: March 1, 2026, 8:12 p.m.