Triple
T1632502
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Los Angeles via Sunset Limited |
E35286
|
entity |
| Predicate | railMode |
P21523
|
FINISHED |
| Object | heavy rail |
—
|
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: heavy rail | Statement: [Los Angeles via Sunset Limited, railMode, heavy rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railMode Context triple: [Los Angeles via Sunset Limited, railMode, heavy rail]
-
A.
commuterRailMode
Indicates that the relationship involves travel or transportation specifically by commuter rail as the mode of transit between the related entities.
-
B.
railServiceType
chosen
Indicates the specific category or type of rail service that applies to the relationship between the involved entities (e.g., local, express, freight).
-
C.
railSystemType
Indicates the specific category or classification of a rail transportation system that an entity belongs to or operates within.
-
D.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
-
E.
railroad
Indicates that one entity constructs, operates, or provides railroad or train transportation services for another entity or area.
- 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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a96083e7308190abbf025fe8e43abb |
completed | March 5, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_69a907cac610819083cafd4396b6d66c |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.