Triple
T13997351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Eligius Hospital |
E336733
|
entity |
| Predicate | hasDepartmentInStory |
P112090
|
FINISHED |
| Object | internal medicine |
—
|
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: internal medicine | Statement: [St. Eligius Hospital, hasDepartmentInStory, internal medicine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDepartmentInStory Context triple: [St. Eligius Hospital, hasDepartmentInStory, internal medicine]
-
A.
hasDepartmentInFiction
Indicates that a fictional work includes or features a specific department as part of its setting or narrative.
-
B.
hasSiblingInStory
Indicates that one character in a narrative has at least one sibling who also appears within the same story.
-
C.
hasThemeInStory
Indicates that a particular theme is present or plays a significant role within a given story.
-
D.
basedInDepartment
Indicates that an entity operates or has its primary affiliation within a specific department.
-
E.
hasPartInNarrative
Indicates that one entity plays a role or participates as a component within the storyline or structure of another narrative entity.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2eb68ba88190bfaf10777d607bf3 |
completed | April 14, 2026, 12:10 p.m. |
| PD | Predicate disambiguation | batch_69dd465dfbc4819090d8c61fd572d35f |
completed | April 13, 2026, 7:39 p.m. |
| PDg | Predicate description generation | batch_69de01ed2098819088ec45069f6f2609 |
completed | April 14, 2026, 8:59 a.m. |
Created at: April 9, 2026, 10:19 p.m.