Triple

T36878456

Position	Surface form	Disambiguated ID	Type / Status
Subject	Nikiya	`E911406`	entity
Predicate	appearsInAct	`P795`	FINISHED
Object	Act II of La Bayadère Act II of La Bayadère is the grand, opulent celebration scene of the ballet in which the temple dancer Nikiya’s tragic fate unfolds amid courtly festivities and betrayal.	`E2205123`	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: Act II of La Bayadère | Statement: [Nikiya, appearsInAct, Act II of La Bayadère]

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: Act II of La Bayadère
Triple: [Nikiya, appearsInAct, Act II of La Bayadère]

Generated description

Act II of *La Bayadère* is the grand, opulent celebration scene of the ballet in which the temple dancer Nikiya’s tragic fate unfolds amid courtly festivities and betrayal.

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_69f76e82339881909607a65c0503d941`	completed	May 3, 2026, 3:49 p.m.
NER	Named-entity recognition	`batch_69f9fd6834cc8190aa27153d6a99f3bb`	completed	May 5, 2026, 2:23 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a3e161dbf74819083712e5038938e3b`	completed	June 26, 2026, 6:03 a.m.
NEDg	Description generation	`batch_6a3e16c7ae008190aed858fd5da64a5d`	completed	June 26, 2026, 6:05 a.m.
NED2	Entity disambiguation (via description)	`batch_6a3e2225d4fc8190baaf1e61f7e1642f`	completed	June 26, 2026, 6:54 a.m.

Created at: May 3, 2026, 4:13 p.m.