Triple
T1932842
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SEPTA Suburban Transit Division |
E40983
|
entity |
| Predicate | hasServiceFrequencyCharacteristic |
P28499
|
FINISHED |
| Object | daily service |
—
|
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: daily service | Statement: [SEPTA Suburban Transit Division, hasServiceFrequencyCharacteristic, daily service]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasServiceFrequencyCharacteristic Context triple: [SEPTA Suburban Transit Division, hasServiceFrequencyCharacteristic, daily service]
-
A.
hasFrequencyCategory
chosen
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
B.
hasCarrierFrequency
Indicates that an entity (such as a signal or transmission) is associated with a specific carrier frequency at which it is transmitted or modulated.
-
C.
usesFrequency
Indicates that one entity employs or operates another entity at a specified rate, interval, or number of occurrences over time.
-
D.
hasEventFrequency
Indicates how often a particular event occurs within a given time period.
-
E.
hasFrequencyNote
Indicates that something is associated with a specific note describing how often it occurs or is repeated.
- 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_69a8864711648190b07bed24ed76258e |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb299f3c48190a5021d320ded4405 |
completed | March 7, 2026, 5:07 a.m. |
| PD | Predicate disambiguation | batch_69abafeec6f881909d47acb966683279 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.