Triple
T31710842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sacred Heart Hospital |
E809311
|
entity |
| Predicate | headNurseCharacter |
P45541
|
FINISHED |
| Object | Carla Espinosa |
—
|
NE NERFINISHED |
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: Carla Espinosa | Statement: [Sacred Heart Hospital, headNurseCharacter, Carla Espinosa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: headNurseCharacter Context triple: [Sacred Heart Hospital, headNurseCharacter, Carla Espinosa]
-
A.
hasNurse
Indicates that an entity is assigned or associated with a nurse who provides care or medical support to it.
-
B.
narrativeCharacter
chosen
Indicates that one entity functions as a character within the narrative or story associated with another entity.
-
C.
headOfSystem
Indicates that one entity serves as the primary leader or top authority in charge of a particular system.
-
D.
hatCharakter
Indicates that an entity possesses or exhibits a particular character, trait, or quality.
-
E.
hasNannyCharacter
Indicates that one entity serves as a nanny or caregiver character for another entity.
- F. None of above.
Provenance (3 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_69f348df4e048190a4a5a9932ada78d6 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6aaf50be08190a2b62a6d881f8aee |
completed | May 3, 2026, 1:55 a.m. |
| PD | Predicate disambiguation | batch_69f6aa20a1588190a53533fc9764efb2 |
completed | May 3, 2026, 1:51 a.m. |
Created at: April 30, 2026, 11:15 p.m.