Triple

T26782985

Position	Surface form	Disambiguated ID	Type / Status
Subject	Sena dynasty	`E670298`	entity
Predicate	notableRuler	`P22`	FINISHED
Object	Keshava Sena Keshava Sena was a prominent ruler of the Sena dynasty, a medieval Indian royal house known for its patronage of Hindu culture and temple architecture in eastern India.	`E1745675`	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: Keshava Sena | Statement: [Sena dynasty, notableRuler, Keshava Sena]

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: Keshava Sena
Triple: [Sena dynasty, notableRuler, Keshava Sena]

Generated description

Keshava Sena was a prominent ruler of the Sena dynasty, a medieval Indian royal house known for its patronage of Hindu culture and temple architecture in eastern India.

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_69eeb31c925881909b597f6e40056d28`	completed	April 27, 2026, 12:51 a.m.
NER	Named-entity recognition	`batch_69f6197d4b3c8190a50621369e08f71d`	completed	May 2, 2026, 3:34 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a12132e1f7c8190b55d411f437e7247`	completed	May 23, 2026, 8:50 p.m.
NEDg	Description generation	`batch_6a121416401481908c0fa6e1c2e9e317`	completed	May 23, 2026, 8:54 p.m.
NED2	Entity disambiguation (via description)	`batch_6a1217e349a08190a986e6ce56f5b82d`	completed	May 23, 2026, 9:10 p.m.

Created at: April 27, 2026, 4:10 a.m.