Triple

T32898901
Position Surface form Disambiguated ID Type / Status
Subject The Glass Room E841551 entity
Predicate stars P1956 FINISHED
Object Alexandra Borbély
Alexandra Borbély is a Slovak-Hungarian actress best known internationally for her acclaimed performances in European art-house films, including the Oscar-nominated "On Body and Soul."
E2031384 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: Alexandra Borbély | Statement: [The Glass Room, stars, Alexandra Borbély]
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: Alexandra Borbély
Triple: [The Glass Room, stars, Alexandra Borbély]
Generated description
Alexandra Borbély is a Slovak-Hungarian actress best known internationally for her acclaimed performances in European art-house films, including the Oscar-nominated "On Body and Soul."

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_69f34945ae408190b72d8118c83beb77 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d076b85481909a0ef6d51c417f6f completed May 3, 2026, 4:35 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34daade43c819080f3755850533bf8 completed June 19, 2026, 5:59 a.m.
NEDg Description generation batch_6a34db3a7bdc81908847422f97af39ef completed June 19, 2026, 6:01 a.m.
NED2 Entity disambiguation (via description) batch_6a34dbbee0988190b9d65aa80f05f035 completed June 19, 2026, 6:03 a.m.
Created at: May 1, 2026, 1:19 a.m.