Triple
T111875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | B Line Hollywood segment |
E2264
|
entity |
| Predicate | fareSystem |
P395
|
FINISHED |
| Object | TAP card |
E11309
|
NE 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: TAP card | Statement: [B Line Hollywood segment, fareSystem, TAP card]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TAP card Context triple: [B Line Hollywood segment, fareSystem, TAP card]
-
A.
TAP card
chosen
The TAP card is a reusable contactless smart card used to pay fares across public transit systems in the Los Angeles County region.
-
B.
SmarTrip
SmarTrip is a rechargeable contactless smart card used to pay fares on the Washington, D.C. region’s public transit systems.
-
C.
MetroCard
MetroCard is a magnetic stripe payment card formerly used as the primary method for paying fares on New York City’s public transit system, including subways and buses.
-
D.
SEPTA Key
SEPTA Key is a contactless smart fare card and payment system used across Philadelphia’s SEPTA public transit network.
-
E.
CharlieCard
The CharlieCard is a reusable contactless smart card used to pay fares on Boston's MBTA public transit system.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a256ec650c8190bee2067e37065527 |
completed | Feb. 28, 2026, 2:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2850039588190a5f3ec0ba1b3cdda |
completed | Feb. 28, 2026, 6:02 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.