Triple

T27449218
Position Surface form Disambiguated ID Type / Status
Subject Helen Vinson E692385 entity
Predicate birthName P65 FINISHED
Object Helen Rulfs
Helen Rulfs, better known by her stage name Helen Vinson, was an American film actress prominent in the 1930s and 1940s, often cast in sophisticated or villainous roles.
 E1846641 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: Helen Rulfs | Statement: [Helen Vinson, birthName, Helen Rulfs]
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: Helen Rulfs
Triple: [Helen Vinson, birthName, Helen Rulfs]
Generated description
Helen Rulfs, better known by her stage name Helen Vinson, was an American film actress prominent in the 1930s and 1940s, often cast in sophisticated or villainous roles.

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_69ef5206c9248190b5975c2a7f9d229c completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62dc4980481909e303ade433c7d61 completed May 2, 2026, 5 p.m.
NED1 Entity disambiguation (via context triple) batch_6a251f403648819082a4b8bc90a77087 completed June 7, 2026, 7:35 a.m.
NEDg Description generation batch_6a2524587f4c8190866c4b0e6e8cf43a completed June 7, 2026, 7:57 a.m.
NED2 Entity disambiguation (via description) batch_6a2524b966888190a20408ad0f27f892 completed June 7, 2026, 7:58 a.m.
Created at: April 27, 2026, 12:47 p.m.