Triple
T24492400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manchester Gorton (UK Parliament constituency) |
E617676
|
entity |
| Predicate | hadInnerCityCharacter |
P154385
|
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: [Manchester Gorton (UK Parliament constituency), hadInnerCityCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadInnerCityCharacter Context triple: [Manchester Gorton (UK Parliament constituency), hadInnerCityCharacter, true]
-
A.
hasInnerCity
Indicates that one entity contains or is associated with a specific inner city within its boundaries or structure.
-
B.
hadCity
Indicates that an entity was formerly associated with or located in a particular city.
-
C.
hasSemiUrbanCharacter
Indicates that something possesses qualities or features characteristic of both urban and rural environments, but not fully urban in nature.
-
D.
hasUrbanFeature
Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
-
E.
hasUrbanAreaCharacter
chosen
Indicates that something possesses qualities, features, or conditions typical of an urban area.
- 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_69e2d7f4e6bc8190aec540ae3b9ed7f2 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:22 a.m.