Triple

T28588728
Position Surface form Disambiguated ID Type / Status
Subject Kondaveedu Fort E723580 entity
Predicate nearbySettlement P350 FINISHED
Object Kondaveedu village
Kondaveedu village is a rural settlement in Andhra Pradesh, India, known primarily for its proximity to the historic Kondaveedu Fort and its surrounding hill ranges.
E1827049 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: Kondaveedu village | Statement: [Kondaveedu Fort, nearbySettlement, Kondaveedu village]
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: Kondaveedu village
Triple: [Kondaveedu Fort, nearbySettlement, Kondaveedu village]
Generated description
Kondaveedu village is a rural settlement in Andhra Pradesh, India, known primarily for its proximity to the historic Kondaveedu Fort and its surrounding hill ranges.

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_69f01d7f92e481909847f5f3f3174a89 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_69f651b1e7fc819090eb5bde76da5093 completed May 2, 2026, 7:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1cc3716ecc81908dae0c9fbb6356f6 completed May 31, 2026, 11:25 p.m.
NEDg Description generation batch_6a1cc4088b408190b5ad487fcdfac2ac completed May 31, 2026, 11:28 p.m.
NED2 Entity disambiguation (via description) batch_6a1cc4b69e5c8190bae7beb6a8b82aa7 completed May 31, 2026, 11:31 p.m.
Created at: April 28, 2026, 4:19 a.m.