Triple

T26560325
Position Surface form Disambiguated ID Type / Status
Subject Fiona Gubelmann E666221 entity
Predicate hasActedIn P15620 FINISHED
Object The Way We Weren't
The Way We Weren't is a romantic comedy film that follows two people who fall in love after each has lied extensively about their past, forcing them to confront the truth when their deceptions unravel.
 E1732846 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 Way We Weren't | Statement: [Fiona Gubelmann, hasActedIn, The Way We Weren't]
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 Way We Weren't
Triple: [Fiona Gubelmann, hasActedIn, The Way We Weren't]
Generated description
The Way We Weren't is a romantic comedy film that follows two people who fall in love after each has lied extensively about their past, forcing them to confront the truth when their deceptions unravel.

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_69ee9cf7e94481909f0d556b36e43572 completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f6146b58f4819082de70318c588211 completed May 2, 2026, 3:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c823f96081909faab64a99d2483b completed May 23, 2026, 3:30 p.m.
NEDg Description generation batch_6a11c919c3d08190ae5cc3a21f5257be completed May 23, 2026, 3:34 p.m.
NED2 Entity disambiguation (via description) batch_6a11ca240174819082559be9b1e08482 completed May 23, 2026, 3:39 p.m.
Created at: April 27, 2026, 1:52 a.m.