Triple
T18585560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | flat fare (Washington Metro) |
E454227
|
entity |
| Predicate | mayHaveVariantFor |
P104759
|
FINISHED |
| Object | reduced-fare riders |
—
|
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: reduced-fare riders | Statement: [flat fare (Washington Metro), mayHaveVariantFor, reduced-fare riders]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayHaveVariantFor Context triple: [flat fare (Washington Metro), mayHaveVariantFor, reduced-fare riders]
-
A.
hasVariantsIn
Indicates that an entity exists in multiple alternative forms or versions within a specified context or set.
-
B.
hasVariant
Indicates that one entity exists as an alternative form, version, or variation of another entity.
-
C.
hasCommercialVariant
Indicates that an entity has a related version that is produced, marketed, or sold commercially.
-
D.
hasVariantSeries
Indicates a relationship where one entity is a variant or alternative series derived from or associated with another series.
-
E.
likelyVariantOf
chosen
Indicates that one entity is probably a variant, version, or alternative form of another entity, based on available evidence or similarity.
- 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_69d8d38ae7e081908a98df1251842402 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e545b0dff08190a3be481faec34a3c |
completed | April 19, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69e478c98d4c81909d37a0e72c6e7bd0 |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:44 a.m.