Triple
T3878699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Line (Los Angeles Metro) |
E92567
|
entity |
| Predicate | atGradeSections |
P52096
|
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: [Blue Line (Los Angeles Metro), atGradeSections, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: atGradeSections Context triple: [Blue Line (Los Angeles Metro), atGradeSections, yes]
-
A.
studentSection
Indicates a relationship where a student is enrolled in or associated with a particular course section.
-
B.
hasStudentSection
Indicates that an entity (such as a course or class) is associated with a specific student section or subgroup of enrolled students.
-
C.
studentSectionName
Indicates that a given student is associated with a specific class section identified by its name.
-
D.
grades
Indicates that one entity evaluates and assigns a score or level of performance to another entity.
-
E.
hasGrades
Indicates that an entity possesses or is associated with one or more grade values, typically reflecting evaluations or scores.
- 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_69aed967448c819086c4b358d37b25aa |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1515c688190a38332aedeed8a76 |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee7574c408190893e70bf80514838 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef14f9bb4819098e64b527b546d74 |
completed | March 9, 2026, 4:11 p.m. |
Created at: March 9, 2026, 3:20 p.m.