Triple
T4644701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Grange University Hospital |
E101738
|
entity |
| Predicate | nearbyRole |
P58875
|
FINISHED |
| Object | primary hospital |
—
|
LITERAL 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: primary hospital | Statement: [Grange University Hospital, nearbyRole, primary hospital]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyRole Context triple: [Grange University Hospital, nearbyRole, primary hospital]
-
A.
nearbyEnvironment
Indicates that an entity is located in or affected by the immediate surrounding conditions or context of another entity.
-
B.
meetsNear
Indicates that two entities meet or come together at a location that is in close proximity to a specified reference point or area.
-
C.
nearbyCurrent
Indicates that one entity is located close to another entity at the present moment or in the current context.
-
D.
nearbyFrontier
Indicates that one entity is located close to a boundary or frontier region associated with another entity.
-
E.
nearPass
Indicates that one entity moves or travels close to another entity without necessarily making direct contact or interaction.
- F. None of above. chosen
Provenance (4 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_69bd43d3bc7c81908f81fcf380476b0f |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6632708c8190b627d99363ab062c |
completed | March 20, 2026, 3:22 p.m. |
| PD | Predicate disambiguation | batch_69bd620fc5e081908325ac8e6a6384ab |
completed | March 20, 2026, 3:04 p.m. |
| PDg | Predicate description generation | batch_69bd663092cc81909308f89ee1a417e4 |
completed | March 20, 2026, 3:22 p.m. |
Created at: March 20, 2026, 1:14 p.m.