Triple
T1280485
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Staten Island Railway |
E27311
|
entity |
| Predicate | gradeSeparation |
P1175
|
FINISHED |
| Object | mostly grade-separated |
—
|
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: mostly grade-separated | Statement: [Staten Island Railway, gradeSeparation, mostly grade-separated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: gradeSeparation Context triple: [Staten Island Railway, gradeSeparation, mostly grade-separated]
-
A.
grades
Indicates that one entity evaluates and assigns a score or level of performance to another entity.
-
B.
gradeNumber
Indicates the numerical grade or level assigned to an entity within an ordered grading or classification system.
-
C.
gradeRank
Indicates the relative academic standing or position of an entity within a graded or ranked group based on performance or scores.
-
D.
separates
chosen
Indicates that one entity divides, parts, or keeps other entities apart from each other.
-
E.
gradeCount
Indicates the number of grades associated with a given entity or context.
- 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_69a496d3710c8190955dee8bc0dacb50 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c094eb4881909a33061339f91190 |
completed | March 1, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69a4bee276d8819092f71c5a1140bb61 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:50 p.m.