Triple
T32121567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North–South Elevated Road |
E820388
|
entity |
| Predicate | hasViaductSections |
P190684
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [North–South Elevated Road, hasViaductSections, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasViaductSections Context triple: [North–South Elevated Road, hasViaductSections, yes]
-
A.
hasViaduct
Indicates that one entity possesses, includes, or is connected by a viaduct in relation to another entity.
-
B.
hasBridgeSection
Indicates that one entity includes or is associated with a specific bridge section as a distinct part or component.
-
C.
hasTunnelSections
Indicates that an entity includes or is composed of multiple distinct tunnel segments or portions.
-
D.
hasBridgeUnderpasses
Indicates that one structure, typically a bridge, includes or provides underpasses that allow passage beneath it.
-
E.
hasNumberOfBridges
Indicates the quantitative relationship specifying how many bridges are associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69f34902d42c819083a8e6bba9a8bb9a |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69fcce2cf9188190b3f65b362203a6a3 |
completed | May 7, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69fcccee6240819084680887731ff64b |
completed | May 7, 2026, 5:33 p.m. |
| PDg | Predicate description generation | batch_69fccdd2d84481909a7ce22407def9c7 |
completed | May 7, 2026, 5:37 p.m. |
Created at: May 1, 2026, 12:28 a.m.