Triple
T10035443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 沙面岛 |
E204952
|
entity |
| Predicate | 相关城市地标 |
P92038
|
FINISHED |
| Object | 广州上下九商业区附近 |
—
|
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: 广州上下九商业区附近 | Statement: [沙面岛, 相关城市地标, 广州上下九商业区附近]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 相关城市地标 Context triple: [沙面岛, 相关城市地标, 广州上下九商业区附近]
-
A.
emblematicBuildingLocation
Indicates that a building serves as a symbolic or representative landmark for a particular location or area.
-
B.
partOfSkylineOf
Indicates that one entity is a visible component or feature contributing to the overall skyline profile of another entity, typically a city or urban area.
-
C.
primaryCityLandmarkOf
Indicates that a landmark is a principal or defining landmark associated with a specific city.
-
D.
includesLandmark
Indicates that one location or area contains or encompasses a specific landmark within its boundaries.
-
E.
cityPanorama
Indicates a wide, comprehensive visual view or representation of a cityscape, typically encompassing many of its features in a single scene.
- 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_69ca834d77188190ad645e33e8ca3200 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdce4a515c8190baec86d924623b12 |
completed | April 2, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69cd4b8638508190b22acc65500ec7d6 |
completed | April 1, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69cd4fed19d481909d2c7ff1114664b6 |
completed | April 1, 2026, 5:03 p.m. |
Created at: March 30, 2026, 8:55 p.m.