Triple
T35737177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 千佛山 |
E1032919
|
entity |
| Predicate | 与城市关系 |
P94117
|
FINISHED |
| Object | 济南三大名胜之一 |
—
|
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: 济南三大名胜之一 | Statement: [千佛山, 与城市关系, 济南三大名胜之一]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 与城市关系 Context triple: [千佛山, 与城市关系, 济南三大名胜之一]
-
A.
相关城市地标
Indicates a relationship where a city landmark is associated with, connected to, or relevant to a given entity or context.
-
B.
相关城市群
Indicates a relationship where an entity is associated with, belongs to, or is relevant to a particular urban agglomeration or city cluster.
-
C.
所在都市
Indicates the city in which an entity is located or based.
-
D.
hasUrbanRelation
chosen
Indicates a relationship where one entity is connected to another through an urban context, such as city-based location, influence, or interaction.
-
E.
urbanRuralRelation
Indicates a relationship that characterizes how urban and rural areas are connected, contrasted, or interact with each other.
- 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_69f76e10e59081908d81ad9ce22f40b6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a34f8ee08190a040304635539a8f |
completed | May 3, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69f7a06f125c8190843af194f042a465 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:05 p.m.