Triple

T28874781
Position Surface form Disambiguated ID Type / Status
Subject Kedah-class offshore patrol vessel E732235 entity
Predicate basedOnDesign P7125 FINISHED
Object MEKO A-100 design
The MEKO A-100 design is a modular, modern warship concept developed by Germany’s Blohm+Voss for small to medium-sized corvettes and offshore patrol vessels, emphasizing flexibility, upgradability, and multi-role capabilities.
 E1835211 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: MEKO A-100 design | Statement: [Kedah-class offshore patrol vessel, basedOnDesign, MEKO A-100 design]
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: MEKO A-100 design
Triple: [Kedah-class offshore patrol vessel, basedOnDesign, MEKO A-100 design]
Generated description
The MEKO A-100 design is a modular, modern warship concept developed by Germany’s Blohm+Voss for small to medium-sized corvettes and offshore patrol vessels, emphasizing flexibility, upgradability, and multi-role capabilities.

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_69f05b06807c81909b4bbd4c20403a2b completed April 28, 2026, 7 a.m.
NER Named-entity recognition batch_69f65a4a48888190b3c1bc721712ae71 completed May 2, 2026, 8:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24bbcb7d74819091a892d27a7fb91d completed June 7, 2026, 12:31 a.m.
NEDg Description generation batch_6a24c13af12081909fdaea65bf79c27a completed June 7, 2026, 12:54 a.m.
NED2 Entity disambiguation (via description) batch_6a24c1aa75c0819080e41fcf600d6d63 completed June 7, 2026, 12:56 a.m.
Created at: April 28, 2026, 7:36 a.m.