Triple
T16918998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 佛香阁 |
E410393
|
entity |
| Predicate | 建筑高度 |
P41605
|
FINISHED |
| Object | 约41米 |
—
|
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: 约41米 | Statement: [佛香阁, 建筑高度, 约41米]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 建筑高度 Context triple: [佛香阁, 建筑高度, 约41米]
-
A.
buildingHeight
chosen
Indicates the vertical extent or height measurement of a building.
-
B.
buildingHeightCharacteristic
Indicates the specific height-related property or measurement that characterizes a building.
-
C.
buildingHeightContext
Indicates the contextual or situational factors under which a building’s height is defined, measured, or interpreted.
-
D.
architecturalHeight
Indicates the measured vertical extent of a structure based on its architectural design, typically from the lowest significant level to the highest architecturally integral point, excluding non-architectural elements like antennas or masts.
-
E.
hasBuildingHeightType
Indicates the classification or type used to characterize the height of a building in the relationship.
- 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_69d886c7b1e481908c3766dfa8c13458 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3cdec3d0c8190994a0fca335c65d6 |
completed | April 18, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69e32b982f548190b08414d55810de19 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:30 a.m.