Triple
T6625796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Denis station |
E149796
|
entity |
| Predicate | fareSystem |
P395
|
FINISHED |
| Object | MARC |
E16027
|
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: MARC | Statement: [St. Denis station, fareSystem, MARC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MARC Context triple: [St. Denis station, fareSystem, MARC]
-
A.
MARC
chosen
MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
-
B.
MARC
MARC is a regional planning and coordination agency serving the Kansas City metropolitan area, focusing on transportation, emergency services, environmental planning, and community development.
-
C.
MARC standards
MARC standards are a set of bibliographic data formats used worldwide to structure and exchange library catalog information in a consistent, machine-readable way.
-
D.
Maschinelles Austauschformat für Bibliotheken
Maschinelles Austauschformat für Bibliotheken (MAB) is a German machine-readable data exchange format historically used by libraries to encode and share bibliographic and authority records.
-
E.
Korean MARC
Korean MARC is a national bibliographic metadata standard used in South Korea for cataloging library and information resources.
- 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_69c687ee50048190aa151765bef16193 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6af8187d881908b7a86f2cae5de23 |
completed | March 27, 2026, 4:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71a65b1648190b1c9d5554927e0cb |
completed | March 28, 2026, 12:01 a.m. |
Created at: March 27, 2026, 1:58 p.m.