Triple
T38234545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bertha of Burgundy |
E1013583
|
entity |
| Predicate | canonLawConflict |
P190585
|
FINISHED |
| Object | consanguinity with Robert II of France |
—
|
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: consanguinity with Robert II of France | Statement: [Bertha of Burgundy, canonLawConflict, consanguinity with Robert II of France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canonLawConflict Context triple: [Bertha of Burgundy, canonLawConflict, consanguinity with Robert II of France]
-
A.
canonLawContext
Indicates that something occurs within, is governed by, or is interpreted according to the norms and framework of canon law.
-
B.
canonLawSubject
Indicates that an entity is the subject or topic governed, regulated, or addressed by a particular canon law or set of canonical legal norms.
-
C.
canonLawAction
Indicates an action, decision, or procedure carried out under or in accordance with canon law.
-
D.
canonLawContribution
Indicates a contribution an entity makes to the development, interpretation, or application of canon law.
-
E.
canonLawTraining
Indicates that one entity has provided or received training or education in canon law in relation to another 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_69f76dd72a248190a5fe18db2bd1eb15 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fccbd826708190b5fab12c4236299a |
completed | May 7, 2026, 5:28 p.m. |
| PD | Predicate disambiguation | batch_69fcc58838e08190b8fa54aa5c165f2d |
completed | May 7, 2026, 5:02 p.m. |
| PDg | Predicate description generation | batch_69fccbd6b7688190b746803cf78d5704 |
completed | May 7, 2026, 5:28 p.m. |
Created at: May 3, 2026, 4:30 p.m.