Triple

T33134397
Position Surface form Disambiguated ID Type / Status
Subject Marie Henriette of Austria E847959 entity
Predicate associatedWith P37 FINISHED
Object Belgian Red Cross
The Belgian Red Cross is the national humanitarian organization of Belgium, part of the International Red Cross and Red Crescent Movement, providing emergency assistance, disaster relief, and health services.
 E2038872 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: Belgian Red Cross | Statement: [Marie Henriette of Austria, associatedWith, Belgian Red Cross]
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: Belgian Red Cross
Triple: [Marie Henriette of Austria, associatedWith, Belgian Red Cross]
Generated description
The Belgian Red Cross is the national humanitarian organization of Belgium, part of the International Red Cross and Red Crescent Movement, providing emergency assistance, disaster relief, and health services.

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_69f3495961d88190b16ea542c2c5f825 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d835000c8190a5731a8ec882ba1e completed May 3, 2026, 5:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a35161f050481908e2dccde163849d5 completed June 19, 2026, 10:12 a.m.
NEDg Description generation batch_6a3519f9aec08190946b94e76cdf8e32 completed June 19, 2026, 10:29 a.m.
NED2 Entity disambiguation (via description) batch_6a351a534c4c819090d75fd53b5db634 completed June 19, 2026, 10:30 a.m.
Created at: May 1, 2026, 1:27 a.m.