Triple
T31855727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gordon (UK Parliament constituency) |
E813189
|
entity |
| Predicate | hasCommuterBeltCharacter |
P133745
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Gordon (UK Parliament constituency), hasCommuterBeltCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommuterBeltCharacter Context triple: [Gordon (UK Parliament constituency), hasCommuterBeltCharacter, true]
-
A.
hasCommuterBelt
chosen
Indicates that one area functions as the commuter belt for another, meaning people regularly travel from the first area to the second for work or daily activities.
-
B.
hasPhysicalBelt
Indicates that one entity possesses or is equipped with a physical belt as an item or feature.
-
C.
usesBeltSystem
Indicates that a subject employs a structured belt-ranking system (e.g., colored belts) to denote levels of skill, progress, or status.
-
D.
beltFeature
Indicates that an entity possesses or is characterized by a specific belt-related attribute or component.
-
E.
beltType
Indicates the specific kind or category of belt associated with an entity.
- 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_69f348ebf32881908d9439646933dc76 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a0324c2d618819093f7e6424b0f5baf |
completed | May 12, 2026, 1:01 p.m. |
| PD | Predicate disambiguation | batch_6a0324292e588190b37d0c3016ea2062 |
completed | May 12, 2026, 12:59 p.m. |
Created at: April 30, 2026, 11:52 p.m.