Triple
T1596721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MetroLink light rail |
E34297
|
entity |
| Predicate | hasRightOfWay |
P29688
|
FINISHED |
| Object | at-grade |
—
|
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: at-grade | Statement: [MetroLink light rail, hasRightOfWay, at-grade]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRightOfWay Context triple: [MetroLink light rail, hasRightOfWay, at-grade]
-
A.
someRightOfWayUsedBy
Indicates that a particular right of way is utilized or traversed by a specified user, route, or transport entity.
-
B.
hasPedestrianPriority
Indicates that pedestrians are given precedence or right-of-way over other road users in a particular context or area.
-
C.
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.
-
D.
hasLegalRight
Indicates that an entity possesses an officially recognized legal entitlement or permission to perform an action or hold a claim regarding another entity.
-
E.
hasTrafficControl
Indicates that some form of traffic management or regulation mechanism is present or applied to a given route, intersection, or transportation element.
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a916d413f08190a4e137e5ed262e25 |
completed | March 5, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69a907bfb39c8190a31e0be14d3d52e6 |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a916d2fae48190aaac6b2a5e31a7cf |
completed | March 5, 2026, 5:38 a.m. |
Created at: March 4, 2026, 7:27 p.m.