Triple

T29826258
Position Surface form Disambiguated ID Type / Status
Subject Alison Klayman E757386 entity
Predicate directorOf P537 FINISHED
Object The 100 Years Show
The 100 Years Show is a documentary film that profiles the life and work of Cuban-American artist Carmen Herrera, celebrating her long-overlooked contributions to modern art.
 E1887435 NE FINISHED

How this triple was built (2 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: The 100 Years Show | Statement: [Alison Klayman, directorOf, The 100 Years Show]
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: The 100 Years Show
Triple: [Alison Klayman, directorOf, The 100 Years Show]
Generated description
The 100 Years Show is a documentary film that profiles the life and work of Cuban-American artist Carmen Herrera, celebrating her long-overlooked contributions to modern art.

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_69f22457c84c8190a6d9f56bc74082a9 completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f67598dff081908ff0ec79a48b55ec completed May 2, 2026, 10:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26e5fecf2881909c598022cdf2ed07 completed June 8, 2026, 3:55 p.m.
NEDg Description generation batch_6a26e6a791bc8190a52d36decacde222 completed June 8, 2026, 3:58 p.m.
NED2 Entity disambiguation (via description) batch_6a26e809c3a88190bba9c39027ab587a completed June 8, 2026, 4:04 p.m.
Created at: April 29, 2026, 5:32 p.m.