Triple
T8906470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 外滩 |
E212071
|
entity |
| Predicate | 所属城市群 |
P73839
|
FINISHED |
| Object | 长三角城市群 |
—
|
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: 长三角城市群 | Statement: [外滩, 所属城市群, 长三角城市群]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 所属城市群 Context triple: [外滩, 所属城市群, 长三角城市群]
-
A.
所在都市
Indicates the city in which an entity is located or based.
-
B.
formsUrbanAreaWith
Indicates that two or more settlements are geographically and functionally connected so that together they constitute a single continuous urban area.
-
C.
hasUrbanAgglomeration
chosen
Indicates that an entity includes, is associated with, or is characterized by a specific urban agglomeration (a densely populated urban area and its surrounding zones).
-
D.
metropolitanAreaType
Indicates the classification of a metropolitan area according to its type or category (e.g., size, function, or administrative status).
-
E.
belongsToUrbanCorridor
Indicates that something is part of, contained within, or functionally integrated into a continuous urban corridor or urbanized area.
- 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_69ca839255248190b43984294abd92ae |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc64c51d6c819098dc33a480dfd462 |
completed | April 1, 2026, 12:20 a.m. |
| PD | Predicate disambiguation | batch_69cc5ecf55248190a29f00fbf99f13c4 |
completed | March 31, 2026, 11:54 p.m. |
Created at: March 30, 2026, 6:55 p.m.