Triple
T214300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MARC standards |
E4784
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | CAN/MARC |
E27069
|
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: CAN/MARC | Statement: [MARC standards, hasComponent, CAN/MARC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CAN/MARC Context triple: [MARC standards, hasComponent, CAN/MARC]
-
A.
MARC
MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
-
B.
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.
-
C.
METS
METS (Metadata Encoding and Transmission Standard) is an XML-based standard for encoding descriptive, administrative, and structural metadata for complex digital library objects.
-
D.
KORMARC
KORMARC is the Korean implementation of the MARC bibliographic data format standard used for cataloging and exchanging library records in Korea.
-
E.
CMARC
chosen
CMARC is the Chinese Machine-Readable Cataloging format, a localized variant of the MARC bibliographic standard used primarily in Chinese-language library cataloging systems.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25c32ae208190a03d504ef43ea659 |
completed | Feb. 28, 2026, 3:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a33e3f66888190a05ddcb0af4d3c5f |
completed | Feb. 28, 2026, 7:13 p.m. |
Created at: Feb. 28, 2026, 2:52 a.m.