Triple
T37388752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 广州国际金融城（规划） |
E928643
|
entity |
| Predicate | 空间属性 |
P36280
|
FINISHED |
| Object | 城市重点功能区 |
—
|
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: 城市重点功能区 | Statement: [广州国际金融城（规划）, 空间属性, 城市重点功能区]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 空间属性 Context triple: [广州国际金融城（规划）, 空间属性, 城市重点功能区]
-
A.
spatialProperty
chosen
Indicates a relationship where one entity has a specific spatial characteristic, such as position, size, orientation, or geometric configuration in space, relative to a reference frame or other entities.
-
B.
spatialRepresentation
Indicates that one entity serves as a spatial depiction, model, or encoding of the location, layout, or geometric properties of another entity.
-
C.
hasSpatialConcept
Indicates a relationship where one entity is associated with, defined by, or characterized through a particular spatial concept or spatial configuration.
-
D.
spatialMetric
Indicates a quantitative relationship that measures spatial properties such as distance, size, or geometric configuration between entities or locations.
-
E.
spatialElements
Indicates a relationship where one or more elements are associated through their positions, arrangement, or relationships within a physical or conceptual space.
- 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_69f76ebb10c481909b54b9dba263e29f |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb9e1845e881908d19158440cf3b87 |
completed | May 6, 2026, 8:01 p.m. |
| PD | Predicate disambiguation | batch_69fb8d08d6988190a00794ac26078348 |
completed | May 6, 2026, 6:48 p.m. |
Created at: May 3, 2026, 4:16 p.m.