Triple

T24552699
Position Surface form Disambiguated ID Type / Status
Subject The Ann Sothern Show E607415 entity
Predicate alsoKnownAs P39 FINISHED
Object The Ann Southern Show
The Ann Sothern Show is an American sitcom from the late 1950s starring Ann Sothern as the efficient and witty assistant manager of a New York hotel.
 E1642099 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: The Ann Southern Show | Statement: [The Ann Sothern Show, alsoKnownAs, The Ann Southern Show]
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: The Ann Southern Show
Triple: [The Ann Sothern Show, alsoKnownAs, The Ann Southern Show]
Generated description
The Ann Sothern Show is an American sitcom from the late 1950s starring Ann Sothern as the efficient and witty assistant manager of a New York hotel.

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_69e2c4cae1b88190825e88d5ce8aa61e completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a8cf1ef88190977da0fb79796616 completed April 30, 2026, 12:56 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0ff85ebe4081908e88d21c55a9a6d5 completed May 22, 2026, 6:31 a.m.
NEDg Description generation batch_6a0ffa0e653481909158d5aab58c47cd completed May 22, 2026, 6:39 a.m.
NED2 Entity disambiguation (via description) batch_6a0ffaba3af88190927957e4d31dd6f7 completed May 22, 2026, 6:42 a.m.
Created at: April 18, 2026, 2:27 a.m.