Triple
T35857931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inspector-General |
E1036558
|
entity |
| Predicate | termMayBe |
P170144
|
FINISHED |
| Object | fixed term of office |
—
|
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: fixed term of office | Statement: [Inspector-General, termMayBe, fixed term of office]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termMayBe Context triple: [Inspector-General, termMayBe, fixed term of office]
-
A.
termType
Indicates the classification or category of a term within a system, specifying what kind of term it is (e.g., type, role, or function) in relation to others.
-
B.
mayHaveTerm
chosen
Indicates that an entity is allowed or able to be associated with a particular term, but is not required to have it.
-
C.
componentTerm
Indicates that one term functions as a component or constituent part of another term within a larger conceptual or structural whole.
-
D.
termPattern
Indicates a relationship where one term follows or matches a specific structural or lexical pattern defined by another term or template.
-
E.
hasTerm
Indicates that an entity includes, is associated with, or is defined by a specific term or condition.
- 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_69f76e1b4aa481909630373171eb5ec6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa3883d48190b05e3d2da7a017ae |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d435288190b30b1991fb003121 |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:06 p.m.