Triple

T23864653
Position Surface form Disambiguated ID Type / Status
Subject Klanjec E592543 entity
Predicate administrativeCenterOf P383 FINISHED
Object Municipality of Klanjec
The Municipality of Klanjec is a local self-government unit in northwestern Croatia centered around the historic town of Klanjec near the Slovenian border.
E1604127 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: Municipality of Klanjec | Statement: [Klanjec, administrativeCenterOf, Municipality of Klanjec]
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: Municipality of Klanjec
Triple: [Klanjec, administrativeCenterOf, Municipality of Klanjec]
Generated description
The Municipality of Klanjec is a local self-government unit in northwestern Croatia centered around the historic town of Klanjec near the Slovenian border.

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_69e25d22eb488190914b193aff952e83 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1cae22ff08190b7085cae21938bb4 completed April 29, 2026, 9:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f69bc874c81908f71d93b3c6cd0eb completed May 21, 2026, 8:23 p.m.
NEDg Description generation batch_6a0f6d42f0dc8190a01c02db0e089d68 completed May 21, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_6a0f6e02703881908fa9c327c5808bf5 completed May 21, 2026, 8:41 p.m.
Created at: April 17, 2026, 8:13 p.m.