Triple

T29019097
Position Surface form Disambiguated ID Type / Status
Subject Walter Forward E737400 entity
Predicate positionHeld P8 FINISHED
Object United States district attorney
The United States district attorney is a federal prosecutor responsible for representing the U.S. government in criminal and civil cases within a specific judicial district.
E1843873 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: United States district attorney | Statement: [Walter Forward, positionHeld, United States district attorney]
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: United States district attorney
Triple: [Walter Forward, positionHeld, United States district attorney]
Generated description
The United States district attorney is a federal prosecutor responsible for representing the U.S. government in criminal and civil cases within a specific judicial district.

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_69f077ee19f881909af48f9cab00a2e5 completed April 28, 2026, 9:03 a.m.
NER Named-entity recognition batch_69f66003a3f48190a2ba6da5aafbb5cb completed May 2, 2026, 8:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2505cf981081909f62473226cd3273 completed June 7, 2026, 5:46 a.m.
NEDg Description generation batch_6a250b60ce248190af70b1f449f36752 completed June 7, 2026, 6:10 a.m.
NED2 Entity disambiguation (via description) batch_6a250c39aaa48190b47a3ae755a63eeb completed June 7, 2026, 6:14 a.m.
Created at: April 28, 2026, 9:48 a.m.