Triple

T33547923
Position Surface form Disambiguated ID Type / Status
Subject Red Cross War Memorial Children’s Hospital E859255 entity
Predicate namedAfter P63 FINISHED
Object Red Cross
The Red Cross is an international humanitarian movement dedicated to providing emergency assistance, disaster relief, and health services to people in need, especially in times of war and crisis.
 E2055998 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: Red Cross | Statement: [Red Cross War Memorial Children’s Hospital, namedAfter, 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: Red Cross
Triple: [Red Cross War Memorial Children’s Hospital, namedAfter, Red Cross]
Generated description
The Red Cross is an international humanitarian movement dedicated to providing emergency assistance, disaster relief, and health services to people in need, especially in times of war and crisis.

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_69f3497a5be08190a39b12736899e034 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f6e9fdb881908324348f29816e49 completed May 3, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a35a68cc7948190bb528e8da54ca499 completed June 19, 2026, 8:29 p.m.
NEDg Description generation batch_6a35a71051648190a33ed02afc5c6798 completed June 19, 2026, 8:31 p.m.
NED2 Entity disambiguation (via description) batch_6a35a7e6c5d4819096edf7666286e717 completed June 19, 2026, 8:34 p.m.
Created at: May 1, 2026, 1:39 a.m.