Triple
T30010535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Statue of Lord Clyde (Colin Campbell) |
E762446
|
entity |
| Predicate | locatedInUrbanSetting |
P82630
|
FINISHED |
| Object | city centre |
—
|
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: city centre | Statement: [Statue of Lord Clyde (Colin Campbell), locatedInUrbanSetting, city centre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInUrbanSetting Context triple: [Statue of Lord Clyde (Colin Campbell), locatedInUrbanSetting, city centre]
-
A.
locatedInUrbanizationType
Indicates that one entity is situated within, or belongs to, a specific type or category of urbanized area (e.g., city, suburb, metropolitan zone).
-
B.
withinUrbanArea
Indicates that one entity is located inside the spatial boundaries of an urban area associated with another entity.
-
C.
appliesToUrbanArea
Indicates that the relationship, rule, or condition is specifically relevant or applicable to an urban area.
-
D.
isUrbanOrNearUrban
Indicates that something is located within an urban area or in close proximity to an urban area.
-
E.
isInUrbanContext
chosen
Indicates that something exists, occurs, or is situated within an urban or city-based environment or setting.
- 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_69f2246b0c84819094f1250b6a02d277 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_6a007dd341108190a1d03eab46041694 |
completed | May 10, 2026, 12:45 p.m. |
| PD | Predicate disambiguation | batch_6a007b1fe2a881909ec50a1e65e4651b |
completed | May 10, 2026, 12:33 p.m. |
Created at: April 29, 2026, 6:44 p.m.