Triple
T11484112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 天河区 |
E272229
|
entity |
| Predicate | 著名地标 |
P92038
|
FINISHED |
| Object | 广州塔附近区域（珠江新城一带） |
—
|
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: 广州塔附近区域（珠江新城一带） | Statement: [天河区, 著名地标, 广州塔附近区域（珠江新城一带）]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 著名地标 Context triple: [天河区, 著名地标, 广州塔附近区域（珠江新城一带）]
-
A.
相关城市地标
chosen
Indicates a relationship where a city landmark is associated with, connected to, or relevant to a given entity or context.
-
B.
emblematicBuildingLocation
Indicates that a building serves as a symbolic or representative landmark for a particular location or area.
-
C.
includesLandmark
Indicates that one location or area contains or encompasses a specific landmark within its boundaries.
-
D.
hasTouristAttractionRole
Indicates that an entity serves in the capacity or function of a tourist attraction for another entity (such as a place, organization, or area).
-
E.
primaryCityLandmarkOf
Indicates that a landmark is a principal or defining landmark associated with a specific city.
- 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85a1ea00c8190b42cdc13a6bc61c3 |
completed | April 10, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69d8086ecd6c81908f424864857762d6 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.