Triple
T3908880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pamphilus of Caesarea |
E87273
|
entity |
| Predicate | roleInLibrary |
P52840
|
FINISHED |
| Object | founder of the library of Caesarea |
—
|
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: founder of the library of Caesarea | Statement: [Pamphilus of Caesarea, roleInLibrary, founder of the library of Caesarea]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInLibrary Context triple: [Pamphilus of Caesarea, roleInLibrary, founder of the library of Caesarea]
-
A.
roleInvolves
Indicates that a particular role includes or requires participation in a specified activity, responsibility, or function.
-
B.
roleInDialogue
Indicates that an entity participates in a dialogue with a specific conversational role (e.g., speaker, listener, moderator) relative to other participants.
-
C.
roleInText
Indicates that an entity participates in a text with a specific function or capacity (e.g., author, editor, character).
-
D.
roleInChapter
Indicates the specific function, position, or part an entity has within a particular chapter of a work.
-
E.
roleInScene
Indicates that an entity participates in a particular scene with a specific role or function within that scene.
- 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1abe2dc81909c18aeae9b286898 |
completed | March 9, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69aee75cff148190b6d5979d17fae085 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef1aada308190821a3dfa6af170b3 |
completed | March 9, 2026, 4:13 p.m. |
Created at: March 9, 2026, 3:22 p.m.