Triple

T38305459

Position	Surface form	Disambiguated ID	Type / Status
Subject	Zoo Authority of Karnataka	`E1032332`	entity
Predicate	oversees	`P46`	FINISHED
Object	Ballari Zoo Ballari Zoo is a regional zoological park in Karnataka, India, known for housing and conserving a variety of native and exotic animal species under state management.	`E2270558`	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: Ballari Zoo | Statement: [Zoo Authority of Karnataka, oversees, Ballari Zoo]

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: Ballari Zoo
Triple: [Zoo Authority of Karnataka, oversees, Ballari Zoo]

Generated description

Ballari Zoo is a regional zoological park in Karnataka, India, known for housing and conserving a variety of native and exotic animal species under state management.

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_69f76e0f2084819091299d021625c3fe`	completed	May 3, 2026, 3:47 p.m.
NER	Named-entity recognition	`batch_69fcc61ddf388190956edc56f74fa49c`	completed	May 7, 2026, 5:04 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a41cc955fe8819090998b30cf7286c7`	completed	June 29, 2026, 1:38 a.m.
NEDg	Description generation	`batch_6a41cdd269d881908e636622d74eee6e`	completed	June 29, 2026, 1:43 a.m.
NED2	Entity disambiguation (via description)	`batch_6a41ce70fe0c8190a627207b8b97ddc5`	completed	June 29, 2026, 1:46 a.m.

Created at: May 3, 2026, 4:30 p.m.