Triple
T17666975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Countess of Forcalquier |
E440407
|
entity |
| Predicate | historicalHolderOccupation |
P128477
|
FINISHED |
| Object | ruler |
—
|
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: ruler | Statement: [Countess of Forcalquier, historicalHolderOccupation, ruler]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: historicalHolderOccupation Context triple: [Countess of Forcalquier, historicalHolderOccupation, ruler]
-
A.
historicalOccupationPattern
Indicates a recurring or characteristic pattern in the occupations held by an entity or its members over historical periods.
-
B.
laterOccupationApproxDate
Indicates an approximate date or time period when a subject began a subsequent occupation or role after an earlier one.
-
C.
originalHolderOccupation
Indicates the occupation or professional role held by the entity that originally possessed or owned another entity.
-
D.
earliestMajorOccupation
Indicates the earliest significant occupation or professional role held by an entity in its life or career timeline.
-
E.
hasHistoricalOccupationMaterial
Indicates that something is composed of or contains material evidence related to past occupations or uses by people.
- 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46eaaaec8819086977d8a5210c44e |
completed | April 19, 2026, 5:56 a.m. |
| PD | Predicate disambiguation | batch_69e3cde007d8819090dd92eea9f022cc |
completed | April 18, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69e3cfaac2b881909e1140339eb1a0dd |
completed | April 18, 2026, 6:38 p.m. |
Created at: April 10, 2026, 9:57 a.m.