Triple
T13972755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xscape Milton Keynes |
E336104
|
entity |
| Predicate | hasSignificantHeight |
P112008
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Xscape Milton Keynes, hasSignificantHeight, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSignificantHeight Context triple: [Xscape Milton Keynes, hasSignificantHeight, yes]
-
A.
hasHeight
Indicates that one entity possesses a specific vertical measurement or stature.
-
B.
hasTowerHeight
Indicates that an entity (such as a tower or structure) has a specific height value associated with it.
-
C.
hasHighRiseSpan
Indicates that a structure or element extends vertically across a significant number of levels or stories, forming a tall or high-rise span.
-
D.
hasSignificant
Indicates that one entity possesses or exhibits a level of importance, impact, or relevance that is notably large or meaningful in relation to another entity or context.
-
E.
hasBuildingHeightType
Indicates the classification or type used to characterize the height of a building in the relationship.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8eae40819080dd4bd25c73b6d6 |
completed | April 14, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69dd465a21408190b912a42c50ffa0d9 |
completed | April 13, 2026, 7:39 p.m. |
| PDg | Predicate description generation | batch_69de01ed2098819088ec45069f6f2609 |
completed | April 14, 2026, 8:59 a.m. |
Created at: April 9, 2026, 10:18 p.m.