Triple
T6602911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edmund Allenby |
E149043
|
entity |
| Predicate | occupationRole |
P2374
|
FINISHED |
| Object | High Commissioner for Egypt and the Sudan |
—
|
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: High Commissioner for Egypt and the Sudan | Statement: [Edmund Allenby, occupationRole, High Commissioner for Egypt and the Sudan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: occupationRole Context triple: [Edmund Allenby, occupationRole, High Commissioner for Egypt and the Sudan]
-
A.
employedRole
Indicates that an entity holds or performs a specific role or position within an employment or work context.
-
B.
subjectOccupation
chosen
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
C.
occupationDuringAlias
Indicates that an entity held a particular occupation specifically during the time period when it was known by a given alias.
-
D.
urbanRole
Indicates the function, status, or role that an entity holds within an urban or city context.
-
E.
recipientOccupation
Indicates that the object specifies the job, profession, or role held by the recipient in the described relationship or event.
- 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_69c687eaa7508190bb58ce2aa02039b3 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acfd17388190bd0bb8b2371e7df1 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:56 p.m.