Triple

T24087414
Position Surface form Disambiguated ID Type / Status
Subject Greed E596685 entity
Predicate starring P1507 FINISHED
Object Dale Fuller
Dale Fuller was an American character actress of the silent film era, known for her expressive supporting roles in early Hollywood cinema.
E1626064 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: Dale Fuller | Statement: [Greed, starring, Dale Fuller]
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: Dale Fuller
Triple: [Greed, starring, Dale Fuller]
Generated description
Dale Fuller was an American character actress of the silent film era, known for her expressive supporting roles in early Hollywood cinema.

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_69e288c4638c81909bacc28a1e3d436b completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1dc2b555481909ffcd2898fdd51fa completed April 29, 2026, 10:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fbcfd7f38819098956dbd3fd1f747 completed May 22, 2026, 2:18 a.m.
NEDg Description generation batch_6a0fc0e6e9588190a2865f94736faa2b completed May 22, 2026, 2:35 a.m.
NED2 Entity disambiguation (via description) batch_6a0fc17855cc8190b4a353b7e94fa0c3 completed May 22, 2026, 2:37 a.m.
Created at: April 17, 2026, 10:45 p.m.