Triple
T3082445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince-Bishopric of Hildesheim |
E64289
|
entity |
| Predicate | hasTerritorialConflict |
P34508
|
FINISHED |
| Object | Hildesheim Diocesan Feud |
—
|
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: Hildesheim Diocesan Feud | Statement: [Prince-Bishopric of Hildesheim, hasTerritorialConflict, Hildesheim Diocesan Feud]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTerritorialConflict Context triple: [Prince-Bishopric of Hildesheim, hasTerritorialConflict, Hildesheim Diocesan Feud]
-
A.
hasPartOfConflict
Indicates that one conflict includes another conflict as a constituent or subordinate part of it.
-
B.
hasMonthOfConflict
Indicates that a conflict is associated with, occurs in, or is characterized by a specific calendar month.
-
C.
conflictRegion
Indicates that the entities are located in or associated with a geographic area characterized by active or recent conflict, tension, or hostilities.
-
D.
militaryConflict
Indicates a relationship where two or more parties are engaged in organized, armed hostilities or warfare against each other.
-
E.
regionOfConflict
chosen
Indicates that a specified region is the location where a particular conflict or dispute takes place.
- 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_69ad857bb4c88190a4cf27893fcabed8 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada1e70b9081908c801d084a6ae992 |
completed | March 8, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69ad9debb6308190be28378ae1fc98af |
completed | March 8, 2026, 4:03 p.m. |
Created at: March 8, 2026, 3:03 p.m.