Triple
T829855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martinsburg station |
E17938
|
entity |
| Predicate | zone |
P2160
|
FINISHED |
| Object | MARC outer zone |
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 outer zone | Statement: [Martinsburg station, zone, MARC outer zone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MARC outer zone Context triple: [Martinsburg station, zone, MARC outer zone]
-
A.
The Zone
The Zone was the lively amusement and entertainment district of the Panama–Pacific International Exposition, featuring rides, shows, and attractions for fairgoers.
-
B.
NORMARC
NORMARC is a Norwegian implementation of the MARC bibliographic metadata standard used by libraries to catalog and exchange information about their collections.
-
C.
MAB
MAB is a German bibliographic data format used for cataloging and exchanging library records, closely related to and historically aligned with MARC standards.
-
D.
KORMARC
KORMARC is the Korean implementation of the MARC bibliographic data format standard used for cataloging and exchanging library records in Korea.
-
E.
MARC
chosen
MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
- 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_69a4937c9c188190aaa216f6b466f452 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abb384988190949d2df65662f76d |
completed | March 1, 2026, 9:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d97a3b08190b7a5c635d74bcd47 |
completed | March 3, 2026, 11:24 p.m. |
Created at: March 1, 2026, 7:38 p.m.