Triple
T24752304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cass County Memorial Hospital |
E619177
|
entity |
| Predicate | operatedBy |
P86
|
FINISHED |
| Object |
Cass Health
Cass Health is a regional healthcare organization that operates medical facilities and services for residents of Cass County and the surrounding area.
|
E1652661
|
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: Cass Health | Statement: [Cass County Memorial Hospital, operatedBy, Cass 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: Cass Health Triple: [Cass County Memorial Hospital, operatedBy, Cass Health]
Generated description
Cass Health is a regional healthcare organization that operates medical facilities and services for residents of Cass County and the surrounding area.
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_69e2fabb349881908a13a212a0221a63 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f4107638dc81909072d5a642094a55 |
completed | May 1, 2026, 2:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a101c0708cc81908ad399ccb3165f58 |
completed | May 22, 2026, 9:04 a.m. |
| NEDg | Description generation | batch_6a10278027908190a4550fe4d788f6f8 |
completed | May 22, 2026, 9:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a10282c01b481908a7340bef6e2a727 |
completed | May 22, 2026, 9:55 a.m. |
Created at: April 18, 2026, 4:25 a.m.