Triple
T37448649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NIOSH Alerts |
E930620
|
entity |
| Predicate | hasDistributionChannel |
P1486
|
FINISHED |
| Object |
NIOSH website
The NIOSH website is the official online platform of the National Institute for Occupational Safety and Health, providing research-based information, guidance, and alerts on workplace safety and health.
|
E2226351
|
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: NIOSH website | Statement: [NIOSH Alerts, hasDistributionChannel, NIOSH website]
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: NIOSH website Triple: [NIOSH Alerts, hasDistributionChannel, NIOSH website]
Generated description
The NIOSH website is the official online platform of the National Institute for Occupational Safety and Health, providing research-based information, guidance, and alerts on workplace safety and health.
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_69f76ec0b9488190b7a4fae632bd1d2f |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb8e06269481909c4516b33fc10b05 |
completed | May 6, 2026, 6:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a408261996481909d3d2c48efea1451 |
completed | June 28, 2026, 2:09 a.m. |
| NEDg | Description generation | batch_6a4082d703748190b0d609d52adca94f |
completed | June 28, 2026, 2:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a40834942708190bd8bd3faa7a8f2c2 |
completed | June 28, 2026, 2:13 a.m. |
Created at: May 3, 2026, 4:17 p.m.