Triple
T314576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British colonial authorities |
E7678
|
entity |
| Predicate | includedOffice |
P11900
|
FINISHED |
| Object | governor |
—
|
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: governor | Statement: [British colonial authorities, includedOffice, governor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includedOffice Context triple: [British colonial authorities, includedOffice, governor]
-
A.
otherOffice
Indicates that one office is an alternative or additional office associated with the same organization, person, or entity as another office.
-
B.
usedByOffice
Indicates that something is utilized, operated, or employed by an office or office-related entity.
-
C.
hasOfficeType
Indicates that an entity’s office is classified as a specific type or category of office.
-
D.
hasOffice
Indicates that an entity possesses or maintains an office at a particular location or within a specific organization.
-
E.
worksWithOffice
Indicates that an entity collaborates or is professionally associated with a particular office or office-based organization.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea62f830819089e94b3aa3e4e187 |
completed | Feb. 28, 2026, 1:15 p.m. |
| PD | Predicate disambiguation | batch_69a2e9428098819089d5950cd2c96dc4 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea08878c8190a5e8a90f620a3888 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:07 p.m.