Triple

T28652608
Position Surface form Disambiguated ID Type / Status
Subject Diocese of Colombo E725239 entity
Predicate hasSchool P113 FINISHED
Object Chundikuli Girls’ College, Jaffna
Chundikuli Girls’ College, Jaffna is a prominent Anglican girls’ school in Jaffna, Sri Lanka, known for its long-standing academic and cultural contributions to the region.
E1829728 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: Chundikuli Girls’ College, Jaffna | Statement: [Diocese of Colombo, hasSchool, Chundikuli Girls’ College, Jaffna]
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: Chundikuli Girls’ College, Jaffna
Triple: [Diocese of Colombo, hasSchool, Chundikuli Girls’ College, Jaffna]
Generated description
Chundikuli Girls’ College, Jaffna is a prominent Anglican girls’ school in Jaffna, Sri Lanka, known for its long-standing academic and cultural contributions to the region.

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_69f01d84f5f0819087ab5e6143b14ed7 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_69f652e613548190a9fd2f9d70489eef completed May 2, 2026, 7:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1ccf3c6160819088803a76998687ab completed June 1, 2026, 12:15 a.m.
NEDg Description generation batch_6a1ccff86fc88190b1438e77f3a5f101 completed June 1, 2026, 12:19 a.m.
NED2 Entity disambiguation (via description) batch_6a24945efab88190a4ccb8a92331e469 completed June 6, 2026, 9:42 p.m.
Created at: April 28, 2026, 4:53 a.m.