Triple
T37062329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yang di-Pertua Negeri of Penang |
E917353
|
entity |
| Predicate | sharesOfficeTypeWith |
P48089
|
FINISHED |
| Object | Yang di-Pertua Negeri of Malacca |
—
|
NE NERFINISHED |
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: Yang di-Pertua Negeri of Malacca | Statement: [Yang di-Pertua Negeri of Penang, sharesOfficeTypeWith, Yang di-Pertua Negeri of Malacca]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesOfficeTypeWith Context triple: [Yang di-Pertua Negeri of Penang, sharesOfficeTypeWith, Yang di-Pertua Negeri of Malacca]
-
A.
sharesOfficeWithPredecessorAndSuccessor
Indicates that an entity occupies an office that was previously held by its predecessor and will subsequently be held by its successor.
-
B.
sharesWorkplaceWith
Indicates that two entities work at the same workplace or organization.
-
C.
sharesBorderWithOffice
Indicates that one entity’s boundary directly adjoins or touches the boundary of an office.
-
D.
sharesTypeWith
chosen
Indicates that two entities belong to the same type or category.
-
E.
sharesBorderWithInOfficeContext
Indicates that two offices or workspaces are directly adjacent to each other, sharing a common boundary or wall within a workplace layout.
- 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_69f76e95fa40819091e14681087ae5e4 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a00ac5654a0819090701f61a5802d64 |
completed | May 10, 2026, 4:03 p.m. |
| PD | Predicate disambiguation | batch_6a00ab94b5e881909e15d1342e5f43be |
completed | May 10, 2026, 4 p.m. |
Created at: May 3, 2026, 4:14 p.m.