Triple

T25548623
Position Surface form Disambiguated ID Type / Status
Subject Gidget Goes to Rome E640376 entity
Predicate follows P134 FINISHED
Object Gidget Goes Hawaiian
Gidget Goes Hawaiian is a 1961 American teen romantic comedy film in the Gidget series, following the surfing-loving heroine on a vacation to Hawaii filled with romantic mix-ups and lighthearted adventures.
 E1691480 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: Gidget Goes Hawaiian | Statement: [Gidget Goes to Rome, follows, Gidget Goes Hawaiian]
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: Gidget Goes Hawaiian
Triple: [Gidget Goes to Rome, follows, Gidget Goes Hawaiian]
Generated description
Gidget Goes Hawaiian is a 1961 American teen romantic comedy film in the Gidget series, following the surfing-loving heroine on a vacation to Hawaii filled with romantic mix-ups and lighthearted adventures.

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_69e75dc101a881909fd33b02174e9768 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f8c398648190be802367a3f5db2d completed May 2, 2026, 1:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10c12d1fd48190b4b23755110d25f4 completed May 22, 2026, 8:48 p.m.
NEDg Description generation batch_6a10c1f3ae208190b3cdc518e83bbc7f completed May 22, 2026, 8:52 p.m.
NED2 Entity disambiguation (via description) batch_6a10c2b0c540819086fe2b0fef3f76d1 completed May 22, 2026, 8:55 p.m.
Created at: April 21, 2026, 3:35 p.m.