Triple
T11705888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English Civil War siege of Donnington Castle |
E278236
|
entity |
| Predicate | garrisonLoyalty |
P24784
|
FINISHED |
| Object | loyal to King Charles I |
—
|
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: loyal to King Charles I | Statement: [English Civil War siege of Donnington Castle, garrisonLoyalty, loyal to King Charles I]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: garrisonLoyalty Context triple: [English Civil War siege of Donnington Castle, garrisonLoyalty, loyal to King Charles I]
-
A.
garrisonSide
chosen
Indicates the side, faction, or allegiance that a garrison is associated with or belongs to.
-
B.
garrisonSize
Indicates the number of troops or defenders stationed at a particular location as its garrison.
-
C.
garrisonDuty
Indicates a relationship where an entity is assigned to stay and defend or guard a specific location as part of its military or protective duties.
-
D.
garrisonState
Indicates that a military force is stationed in or assigned to defend a particular state or territory.
-
E.
garrisonServed
Indicates that a military unit or personnel were stationed at and performed service in a particular garrison or fortified location.
- 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_69d6aaff2ce88190b4a1e4b341ad5377 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a49c8c38819083d83f5fdec52b7f |
completed | April 10, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69d88a7b30948190b616a9db5c5488d5 |
completed | April 10, 2026, 5:28 a.m. |
Created at: April 8, 2026, 9:40 p.m.