Triple
T5509157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cayetana Fitz-James Stuart, 18th Duchess of Alba |
E144516
|
entity |
| Predicate | numberOfGrandeeships |
P65152
|
FINISHED |
| Object | over 40 |
—
|
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: over 40 | Statement: [Cayetana Fitz-James Stuart, 18th Duchess of Alba, numberOfGrandeeships, over 40]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfGrandeeships Context triple: [Cayetana Fitz-James Stuart, 18th Duchess of Alba, numberOfGrandeeships, over 40]
-
A.
termCountAsGrandVizier
Indicates the number of distinct terms an individual has served in the role of Grand Vizier.
-
B.
numberOfConsulships
Indicates the total count of times an entity has held the office or role of consul.
-
C.
numberOfExecutives
Indicates the total count of executives associated with a given entity or context.
-
D.
numberOfDesignations
Indicates the count of distinct designations or titles associated with a given entity.
-
E.
firstOfficeHoldersCount
Indicates the number of individuals who initially held a particular office or position.
- 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_69c008f6b5048190a09064116062cf69 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f4a80d88190bab0056c4c78be93 |
completed | March 22, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69c01b07bde08190b3933b96bdc70dd5 |
completed | March 22, 2026, 4:38 p.m. |
| PDg | Predicate description generation | batch_69c01f051e508190b3886d87b4afdd0b |
completed | March 22, 2026, 4:55 p.m. |
Created at: March 22, 2026, 3:33 p.m.