Triple
T1845225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen Alia al-Hussein |
E41270
|
entity |
| Predicate | ordinalInRole |
P4901
|
FINISHED |
| Object | third wife of King Hussein of Jordan |
—
|
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: third wife of King Hussein of Jordan | Statement: [Queen Alia al-Hussein, ordinalInRole, third wife of King Hussein of Jordan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ordinalInRole Context triple: [Queen Alia al-Hussein, ordinalInRole, third wife of King Hussein of Jordan]
-
A.
ordinalInOffice
Indicates the numerical order or rank of an individual’s term or tenure in a particular office or position.
-
B.
ordinalNumber
chosen
Indicates the position or rank of an entity within an ordered sequence (e.g., first, second, third).
-
C.
orthographicRole
Indicates the functional role that a written form or spelling plays within an orthographic system (e.g., as a letter, diacritic, punctuation mark, or other script element).
-
D.
rankIndicated
Indicates that one entity specifies, denotes, or reveals the hierarchical rank or level of another entity.
-
E.
positionOnStateRole
Indicates that an entity holds or occupies a specific role or position within a governmental or state-related organizational structure.
- 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_69a88648cd44819093303206d96d76ad |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb32d35508190bf1c487dffbecaf0 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafdca6d8819083c66f3a29fd9fd1 |
completed | March 7, 2026, 4:55 a.m. |
Created at: March 4, 2026, 7:33 p.m.