Triple
T1665930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanghai Museum |
E36011
|
entity |
| Predicate | hasTotalFloorArea |
P24212
|
FINISHED |
| Object | approximately 39,000 square meters |
—
|
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: approximately 39,000 square meters | Statement: [Shanghai Museum, hasTotalFloorArea, approximately 39,000 square meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTotalFloorArea Context triple: [Shanghai Museum, hasTotalFloorArea, approximately 39,000 square meters]
-
A.
hasFloorArea
chosen
Indicates that an entity possesses a specified amount of floor space as a measurable area.
-
B.
roofArea
Indicates the total surface area covered by the roof of a structure.
-
C.
grossLeasableArea
Indicates the total floor area within a property that is available to be leased to tenants, excluding common or non-leasable spaces.
-
D.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
numberOfFloors
Indicates the total count of distinct floor levels that a building or structure has.
- 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_69a8861286808190939afff3ce8ee31e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa994f92b0819084ee2f6a672334b9 |
completed | March 6, 2026, 9:07 a.m. |
| PD | Predicate disambiguation | batch_69a907d2475c8190b7ec7dccd3335eb1 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:29 p.m.