Triple
T38452674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. federal government appropriations via NIH |
E912214
|
entity |
| Predicate | overseenBy |
P86
|
FINISHED |
| Object |
House Appropriations Subcommittee on Labor, Health and Human Services, Education, and Related Agencies
The House Appropriations Subcommittee on Labor, Health and Human Services, Education, and Related Agencies is a U.S. congressional panel that drafts and oversees the annual federal spending bills for major domestic programs in health, education, and labor, including funding for agencies like the NIH.
|
E2269787
|
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: House Appropriations Subcommittee on Labor, Health and Human Services, Education, and Related Agencies | Statement: [U.S. federal government appropriations via NIH, overseenBy, House Appropriations Subcommittee on Labor, Health and Human Services, Education, and Related Agencies]
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: House Appropriations Subcommittee on Labor, Health and Human Services, Education, and Related Agencies Triple: [U.S. federal government appropriations via NIH, overseenBy, House Appropriations Subcommittee on Labor, Health and Human Services, Education, and Related Agencies]
Generated description
The House Appropriations Subcommittee on Labor, Health and Human Services, Education, and Related Agencies is a U.S. congressional panel that drafts and oversees the annual federal spending bills for major domestic programs in health, education, and labor, including funding for agencies like the NIH.
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_69f76e84e2dc81908badf05b3aafa9ea |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fccdffc43c8190a4c24316e19d071b |
completed | May 7, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41c2a3cbd08190b5a0654350438547 |
completed | June 29, 2026, 12:56 a.m. |
| NEDg | Description generation | batch_6a41c40a3de08190946014ea3310a85d |
completed | June 29, 2026, 1:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a41c4f387a48190a1696fb010430f69 |
completed | June 29, 2026, 1:05 a.m. |
Created at: May 3, 2026, 4:31 p.m.