Triple
T29206521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RER NG |
E740425
|
entity |
| Predicate | targetServiceFrequency |
P61818
|
FINISHED |
| Object | high-frequency urban and suburban 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: high-frequency urban and suburban service | Statement: [RER NG, targetServiceFrequency, high-frequency urban and suburban service]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetServiceFrequency Context triple: [RER NG, targetServiceFrequency, high-frequency urban and suburban service]
-
A.
serviceFrequencyContext
chosen
Indicates the contextual conditions or circumstances under which a service’s frequency is defined, applied, or interpreted.
-
B.
serviceFrequencyType
Indicates how often a service occurs or is scheduled within a given time period.
-
C.
measurementFrequency
Indicates how often a measurement is taken or recorded over time.
-
D.
seriesFrequency
Indicates how often the events or items in a recurring series occur over time.
-
E.
laterFrequency
Indicates that one event, state, or action occurs with a lower frequency than another in a temporal sequence.
- 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_69f07cb974108190b7e86ca489a6ebb6 |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69fbc36ce1f88190a7fa1656b714e107 |
completed | May 6, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69fbbd13595c81908719f52c3d37a7e8 |
completed | May 6, 2026, 10:13 p.m. |
Created at: April 28, 2026, 12:09 p.m.