Triple
T1134739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lexington Avenue Line |
E23112
|
entity |
| Predicate | dailyRidershipRank |
P17463
|
FINISHED |
| Object | highest among NYC Subway trunk 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: highest among NYC Subway trunk lines | Statement: [Lexington Avenue Line, dailyRidershipRank, highest among NYC Subway trunk lines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dailyRidershipRank Context triple: [Lexington Avenue Line, dailyRidershipRank, highest among NYC Subway trunk lines]
-
A.
dailyRidership
Indicates the typical number of people who use or ride a given transportation service each day.
-
B.
dailyRidershipPeak
Indicates that the relationship specifies the highest number of riders or users recorded for a service or system within a single day.
-
C.
annualRidership
Indicates the total number of passengers who use a transportation service over the course of one year.
-
D.
dailyRidershipCategory
chosen
Indicates the classification of an entity based on the typical number of riders it serves per day.
-
E.
hasApproxAnnualPassengerUsageRank
Indicates the approximate position or ranking of an entity based on its annual passenger usage compared to similar 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_69a493ec75988190b63a11bafaec29b4 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bde18d208190848c189b2b8d585f |
completed | March 1, 2026, 10:29 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4b52d48190bec2e7ad1cc8efc0 |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:44 p.m.