Triple

T36977988
Position Surface form Disambiguated ID Type / Status
Subject Occupational Information Network E914749 entity
Predicate replaced P101 FINISHED
Object Dictionary of Occupational Titles
The Dictionary of Occupational Titles was a comprehensive U.S. Department of Labor reference that systematically classified and described thousands of jobs in the American workforce.
 E2206129 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: Dictionary of Occupational Titles | Statement: [Occupational Information Network, replaced, Dictionary of Occupational Titles]
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: Dictionary of Occupational Titles
Triple: [Occupational Information Network, replaced, Dictionary of Occupational Titles]
Generated description
The Dictionary of Occupational Titles was a comprehensive U.S. Department of Labor reference that systematically classified and described thousands of jobs in the American workforce.

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_69f76e8d13b4819089af24a47ce092fc completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9ff7d6a1c8190bed9732e85420d0e completed May 5, 2026, 2:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e2c4be7448190849a415a06c78f0e completed June 26, 2026, 7:37 a.m.
NEDg Description generation batch_6a3e2d0b7b9081908b0a1754dfbea0df completed June 26, 2026, 7:40 a.m.
NED2 Entity disambiguation (via description) batch_6a3e40f1c27c8190aacf64bbd31eb44b completed June 26, 2026, 9:05 a.m.
Created at: May 3, 2026, 4:14 p.m.