Triple
T26148466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goulburn Valley Health |
E659745
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
GV Health
GV Health is a regional public health service based in the Goulburn Valley area of Victoria, Australia, providing hospital and community healthcare to local communities.
|
E1711721
|
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: GV Health | Statement: [Goulburn Valley Health, shortName, GV Health]
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: GV Health Triple: [Goulburn Valley Health, shortName, GV Health]
Generated description
GV Health is a regional public health service based in the Goulburn Valley area of Victoria, Australia, providing hospital and community healthcare to local communities.
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_69ee5bc496a88190af7deb7ab5e081de |
completed | April 26, 2026, 6:39 p.m. |
| NER | Named-entity recognition | batch_69f60bea4cc8819080c1785709c275cb |
completed | May 2, 2026, 2:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11276e7c088190b1f83bb1ea01ebb8 |
completed | May 23, 2026, 4:05 a.m. |
| NEDg | Description generation | batch_6a11388110a48190a4e9eda80f6e6f29 |
completed | May 23, 2026, 5:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a113935af5c819092a46cfc69ddcc14 |
completed | May 23, 2026, 5:20 a.m. |
Created at: April 26, 2026, 8:23 p.m.