Triple
T15231954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marchesa |
E364024
|
entity |
| Predicate | nobilityRankOrder |
P53396
|
FINISHED |
| Object | between count and duke |
—
|
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: between count and duke | Statement: [Marchesa, nobilityRankOrder, between count and duke]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nobilityRankOrder Context triple: [Marchesa, nobilityRankOrder, between count and duke]
-
A.
nobleRankInOrder
Indicates that an entity holds a specific noble rank at a particular position within an established order of nobility.
-
B.
nobilitySystem
Indicates a social or political structure in which individuals are ranked by hereditary titles or noble status.
-
C.
nobilityClass
Indicates that an entity belongs to, or is associated with, a particular class or rank within a nobility hierarchy.
-
D.
nobleRankIn
Indicates that an entity holds a specified noble rank within a particular political or territorial jurisdiction.
-
E.
nobleRankType
chosen
Indicates the specific category or level of nobility associated with an entity within a hierarchical noble rank system.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0078e27408190bc13c0ca441f5594 |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca899d5c8190be4a7c71e1683c69 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:12 a.m.