Triple

T30591712
Position Surface form Disambiguated ID Type / Status
Subject June Haver E778674 entity
Predicate alsoKnownAs P39 FINISHED
Object June Haver MacMurray
June Haver MacMurray was an American film actress and singer of the 1940s and 1950s, known for her musical roles at 20th Century Fox and later marriage to actor Fred MacMurray.
E1929937 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: June Haver MacMurray | Statement: [June Haver, alsoKnownAs, June Haver MacMurray]
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: June Haver MacMurray
Triple: [June Haver, alsoKnownAs, June Haver MacMurray]
Generated description
June Haver MacMurray was an American film actress and singer of the 1940s and 1950s, known for her musical roles at 20th Century Fox and later marriage to actor Fred MacMurray.

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_69f224a1570c8190a85d3ac330479a79 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f6897bc52481908122b1af6cb45526 completed May 2, 2026, 11:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2898c61cd48190a131b450936b7025 completed June 9, 2026, 10:50 p.m.
NEDg Description generation batch_6a289987c9988190a355050ae3113a08 completed June 9, 2026, 10:53 p.m.
NED2 Entity disambiguation (via description) batch_6a289d68dafc8190a4624b6bc4f54b9c completed June 9, 2026, 11:10 p.m.
Created at: April 29, 2026, 8:24 p.m.