Triple
T38564453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blanche of Bourbon |
E928176
|
entity |
| Predicate | conflictOrEvent |
P12
|
FINISHED |
| Object | Castilian Civil War (context of her life) |
E482962
|
NE 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: Castilian Civil War (context of her life) | Statement: [Blanche of Bourbon, conflictOrEvent, Castilian Civil War (context of her life)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: conflictOrEvent Context triple: [Blanche of Bourbon, conflictOrEvent, Castilian Civil War (context of her life)]
-
A.
politicalConflict
Indicates a relationship where entities are engaged in opposing political positions, struggles, or disputes, often involving competition for power, influence, or policy outcomes.
-
B.
loreConflict
Indicates that there is an inconsistency, contradiction, or incompatibility between pieces of lore or canonical information.
-
C.
conflictCountry
Indicates that there is an armed conflict or war involving the referenced country as a participant.
-
D.
militaryConflict
chosen
Indicates a relationship where two or more parties are engaged in organized, armed hostilities or warfare against each other.
-
E.
contemporaryConflict
Indicates a conflict or dispute occurring between entities within the same general time period or historical era.
- F. None of above.
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_69f76eb8d1808190a588af29d8b266d6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdaa36f90819093f8661969990c7d |
completed | May 7, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41e04552ac81909d54f56dc69cf5a4 |
completed | June 29, 2026, 3:02 a.m. |
| PD | Predicate disambiguation | batch_69fcd8fefc588190b063d7ea1ec87b07 |
completed | May 7, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:32 p.m.