Triple
T6608099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dock10 |
E149168
|
entity |
| Predicate | hasLargestStudioAreaSquareMetres |
P72415
|
FINISHED |
| Object | 12000 |
—
|
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: 12000 | Statement: [Dock10, hasLargestStudioAreaSquareMetres, 12000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLargestStudioAreaSquareMetres Context triple: [Dock10, hasLargestStudioAreaSquareMetres, 12000]
-
A.
hasLargestAreaOf
Indicates that the subject entity possesses the greatest area (size of surface or region) compared to the other entities in the specified set or context.
-
B.
grossLeasableArea
Indicates the total floor area within a property that is available to be leased to tenants, excluding common or non-leasable spaces.
-
C.
hasFloorArea
Indicates that an entity possesses a specified amount of floor space as a measurable area.
-
D.
hasCasinoFloorArea
Indicates the total floor area occupied by a casino within a given property or facility.
-
E.
standardArea
Indicates that an entity has a designated or officially defined area or size that serves as a standard reference.
- 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_69c687eaa7508190bb58ce2aa02039b3 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cf3796d08190a26e988386089447 |
completed | March 27, 2026, 6:40 p.m. |
| PD | Predicate disambiguation | batch_69c6acfed25481909cac74c84a9fe088 |
completed | March 27, 2026, 4:14 p.m. |
| PDg | Predicate description generation | batch_69c6cf3683d08190b19e2aad30f2800f |
completed | March 27, 2026, 6:40 p.m. |
Created at: March 27, 2026, 1:57 p.m.