Triple
T271503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MAX Yellow Line |
E5642
|
entity |
| Predicate | hasTypeOfService |
P849
|
FINISHED |
| Object | urban rail transit |
—
|
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: urban rail transit | Statement: [MAX Yellow Line, hasTypeOfService, urban rail transit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfService Context triple: [MAX Yellow Line, hasTypeOfService, urban rail transit]
-
A.
hasServiceType
chosen
Indicates that an entity is associated with or categorized by a particular type of service.
-
B.
hasServiceTo
Indicates that one entity provides, offers, or operates a service for or directed toward another entity.
-
C.
hasServiceClass
Indicates that an entity is associated with, or categorized under, a particular class or type of service.
-
D.
hasOperationType
Indicates the specific kind or category of operation associated with an entity or process.
-
E.
tollingType
Indicates the specific method or basis by which a toll, fee, or charge is applied or calculated in a given 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_69a25853594c8190b05ec3a586ec88bf |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25e69a9248190b9e7959b43223baa |
completed | Feb. 28, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69a25b721180819080d43c43fcbccf87 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:57 a.m.