Triple
T36978151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quarterly Census of Employment and Wages |
E914751
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object |
ES-202 program
The ES-202 program was a U.S. labor statistics initiative that collected detailed employment and wage data from employers, later renamed and expanded as the Quarterly Census of Employment and Wages (QCEW).
|
E2206133
|
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: ES-202 program | Statement: [Quarterly Census of Employment and Wages, formerName, ES-202 program]
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: ES-202 program Triple: [Quarterly Census of Employment and Wages, formerName, ES-202 program]
Generated description
The ES-202 program was a U.S. labor statistics initiative that collected detailed employment and wage data from employers, later renamed and expanded as the Quarterly Census of Employment and Wages (QCEW).
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_69f9ff7fac308190b3645a75351a459a |
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.