Triple
T3779875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | General Hospital |
E85390
|
entity |
| Predicate | originallySetIn |
P13656
|
FINISHED |
| Object | a general hospital in an unnamed city |
—
|
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: a general hospital in an unnamed city | Statement: [General Hospital, originallySetIn, a general hospital in an unnamed city]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originallySetIn Context triple: [General Hospital, originallySetIn, a general hospital in an unnamed city]
-
A.
originallyIn
chosen
Indicates that something first appeared, was created, or was initially located within a particular context, source, or place.
-
B.
originallyHad
Indicates that an entity previously possessed, contained, or was associated with something before a change, loss, or transformation occurred.
-
C.
originallyBy
Indicates that one entity is the original creator, author, or source of another entity.
-
D.
originallyBuilt
Indicates that one entity was the first or initial builder or constructor of another entity.
-
E.
cityOfOriginal
Indicates the city from which something or someone originally comes or was first created or established.
- 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_69aed937fa8881908208ef3801060826 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee634c6ac819099653c660c286746 |
completed | March 9, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69aee3d3c92c819081d9d5c45ef37a5d |
completed | March 9, 2026, 3:14 p.m. |
Created at: March 9, 2026, 3:12 p.m.