Triple
T12722879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MRT Yellow Line |
E304028
|
entity |
| Predicate | hasTypicalRightOfWay |
P106573
|
FINISHED |
| Object | exclusive right-of-way |
—
|
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: exclusive right-of-way | Statement: [MRT Yellow Line, hasTypicalRightOfWay, exclusive right-of-way]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalRightOfWay Context triple: [MRT Yellow Line, hasTypicalRightOfWay, exclusive right-of-way]
-
A.
hasRightOfWay
Indicates that one entity is entitled to proceed or act before another in a shared space or interaction, without having to yield.
-
B.
hadRightOfWay
Indicates that one party was entitled, by rule or law, to proceed first or continue without yielding in a shared space or traffic situation.
-
C.
someRightOfWayUsedBy
Indicates that a particular right of way is utilized or traversed by a specified user, route, or transport entity.
-
D.
ownerOfRightOfWay
Indicates that one entity holds the legal right to pass through or use a specific path, route, or area on another entity’s property.
-
E.
hasPedestrianPriority
Indicates that pedestrians are given precedence or right-of-way over other road users in a particular context or area.
- 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_69d7bdf084148190ab9d513dc0735af4 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d89ea70819098c470344f172167 |
completed | April 10, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69d96403957c81909acdee7bdae71696 |
completed | April 10, 2026, 8:56 p.m. |
| PDg | Predicate description generation | batch_69d96d87078c819083ea724238992204 |
completed | April 10, 2026, 9:37 p.m. |
Created at: April 9, 2026, 5:24 p.m.