Triple

T35703693
Position Surface form Disambiguated ID Type / Status
Subject Beauty and the Beast (2012 TV series) E1031656 entity
Predicate creator P184 FINISHED
Object Sherri Cooper-Landsman
Sherri Cooper-Landsman is an American television writer and producer best known for co-creating the 2012 fantasy drama series "Beauty and the Beast."
E2225161 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: Sherri Cooper-Landsman | Statement: [Beauty and the Beast (2012 TV series), creator, Sherri Cooper-Landsman]
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: Sherri Cooper-Landsman
Triple: [Beauty and the Beast (2012 TV series), creator, Sherri Cooper-Landsman]
Generated description
Sherri Cooper-Landsman is an American television writer and producer best known for co-creating the 2012 fantasy drama series "Beauty and the Beast."

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_69f76e0d393c8190b6303c64408736db completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a0c8143881909b4d1e4eef946799 completed May 3, 2026, 7:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4076d91da88190a12ed4914ae18f5c completed June 28, 2026, 1:20 a.m.
NEDg Description generation batch_6a40776b75bc8190aa748bc0aae9abbf completed June 28, 2026, 1:22 a.m.
NED2 Entity disambiguation (via description) batch_6a4077dd15088190a2c22c8e1ca89036 completed June 28, 2026, 1:24 a.m.
Created at: May 3, 2026, 4:05 p.m.