Triple

T37573197
Position Surface form Disambiguated ID Type / Status
Subject All for Juan, Juan for All: Sugod Bahay E934742 entity
Predicate notableFor P22 FINISHED
Object catchphrase "Sugod Bahay"
"Sugod Bahay" is a popular Filipino television catchphrase associated with surprise home-visit segments that bring games, prizes, and entertainment directly to viewers’ neighborhoods.
E2231793 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: catchphrase "Sugod Bahay" | Statement: [All for Juan, Juan for All: Sugod Bahay, notableFor, catchphrase "Sugod Bahay"]
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: catchphrase "Sugod Bahay"
Triple: [All for Juan, Juan for All: Sugod Bahay, notableFor, catchphrase "Sugod Bahay"]
Generated description
"Sugod Bahay" is a popular Filipino television catchphrase associated with surprise home-visit segments that bring games, prizes, and entertainment directly to viewers’ neighborhoods.

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_69f76ecd99148190be327e391a70f5b6 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba4ba69448190b5a6c653a922dd31 completed May 6, 2026, 8:29 p.m.
NED1 Entity disambiguation (via context triple) batch_6a409f2093608190ab6a6e4ffdcdb9f9 completed June 28, 2026, 4:12 a.m.
NEDg Description generation batch_6a409f8c9e308190bc3a94baf5339163 completed June 28, 2026, 4:14 a.m.
NED2 Entity disambiguation (via description) batch_6a40a038803881908dc2126235ecc6aa completed June 28, 2026, 4:16 a.m.
Created at: May 3, 2026, 4:17 p.m.