Triple
T1756145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro Rail (Los Angeles) |
E38551
|
entity |
| Predicate | serviceFrequencyCharacteristic |
P16914
|
FINISHED |
| Object | frequent all-day service on most lines |
—
|
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: frequent all-day service on most lines | Statement: [Metro Rail (Los Angeles), serviceFrequencyCharacteristic, frequent all-day service on most lines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serviceFrequencyCharacteristic Context triple: [Metro Rail (Los Angeles), serviceFrequencyCharacteristic, frequent all-day service on most lines]
-
A.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
B.
performedFrequency
chosen
Indicates how often an action or activity is carried out within a given time period.
-
C.
serviceCharacterization
Indicates how a service is defined, described, or classified in terms of its properties, behavior, or role.
-
D.
serviceAreaCharacteristic
Indicates a relationship where a service area is associated with a specific attribute or feature that characterizes it.
-
E.
isFrequently
Indicates that an action, state, or relationship occurs often or with high regularity between the related entities.
- 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_69a8862bdb2081908aefe831c8aa8017 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba6a63f588190b53b39c6b97d74f4 |
completed | March 7, 2026, 4:16 a.m. |
| PD | Predicate disambiguation | batch_69aa61c7ef4c8190abec87c96a787d82 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.