Triple

T20117165
Position Surface form Disambiguated ID Type / Status
Subject Palmy Days E490495 entity
Predicate starring P1507 FINISHED
Object Barbara Weeks
Barbara Weeks was an American film actress active in the 1930s, known for her roles in Hollywood comedies and dramas.
 E1674178 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: Barbara Weeks | Statement: [Palmy Days, starring, Barbara Weeks]
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: Barbara Weeks
Triple: [Palmy Days, starring, Barbara Weeks]
Generated description
Barbara Weeks was an American film actress active in the 1930s, known for her roles in Hollywood comedies and dramas.

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_69da62636cc08190982cc71733a17b8d completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e6673ab4e08190b76ec742605e103b completed April 20, 2026, 5:49 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10759886d88190997a6a6a026b4f89 completed May 22, 2026, 3:26 p.m.
NEDg Description generation batch_6a10765abfb881908ab8908e1e497f64 completed May 22, 2026, 3:29 p.m.
NED2 Entity disambiguation (via description) batch_6a107735ae30819095bf24d523279c69 completed May 22, 2026, 3:33 p.m.
Created at: April 11, 2026, 11:29 p.m.