Triple
T27932712
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | G50 Expressway |
E708025
|
entity |
| Predicate | connectsMegacity |
P56161
|
FINISHED |
| Object | Chongqing |
—
|
NE NERFINISHED |
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: Chongqing | Statement: [G50 Expressway, connectsMegacity, Chongqing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsMegacity Context triple: [G50 Expressway, connectsMegacity, Chongqing]
-
A.
connectsMetroAreas
Indicates a relationship where a transportation route or service links two or more metropolitan areas, enabling direct travel or interaction between them.
-
B.
connectsMajorCity
chosen
Indicates that one entity serves as a link or route providing direct connection to a major city.
-
C.
connectsCity
Indicates a relationship where one entity serves as a link or route that joins or provides direct access between two cities.
-
D.
connectsLargestCitiesOf
Indicates a relationship where something (typically a route, network, or infrastructure) links together the largest cities within a specified region or set.
-
E.
connectsCityTo
Indicates a relationship in which a route, infrastructure, or link joins one city to another, enabling connection or interaction between them.
- 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_69ef96bbf2c48190a9d0e0291457aab6 |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69fd0b92f42881908cd77e3f058adcc2 |
completed | May 7, 2026, 10 p.m. |
| PD | Predicate disambiguation | batch_69fd0a3d68d4819094d92040f7c48d7c |
completed | May 7, 2026, 9:55 p.m. |
Created at: April 27, 2026, 7:04 p.m.