Triple

T26603415
Position Surface form Disambiguated ID Type / Status
Subject Army Service Forces E667700 entity
Predicate oversaw P760 FINISHED
Object Medical Department
The Medical Department was the branch of the United States Army responsible for providing medical care, sanitation, and health services to soldiers and military personnel.
 E1732095 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: Medical Department | Statement: [Army Service Forces, oversaw, Medical Department]
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: Medical Department
Triple: [Army Service Forces, oversaw, Medical Department]
Generated description
The Medical Department was the branch of the United States Army responsible for providing medical care, sanitation, and health services to soldiers and military personnel.

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_69ee9cfd20348190bb1255d2603efb7a completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f6157198fc819083151a884187abae completed May 2, 2026, 3:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11c8415e048190a6e94d953734c0eb completed May 23, 2026, 3:31 p.m.
NEDg Description generation batch_6a11c8f290bc8190bfa1990ee7119516 completed May 23, 2026, 3:34 p.m.
NED2 Entity disambiguation (via description) batch_6a11ca6f162c8190a8c7fbc1e188ea90 completed May 23, 2026, 3:40 p.m.
Created at: April 27, 2026, 2:13 a.m.