Triple
T3758953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Library of Congress Linked Data Service |
E82115
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | MARC Countries |
E4784
|
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 Countries | Statement: [Library of Congress Linked Data Service, hasPart, MARC Countries]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MARC Countries Context triple: [Library of Congress Linked Data Service, hasPart, MARC Countries]
-
A.
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.
-
B.
MARC
MARC is a commuter rail service in Maryland that connects Washington, D.C. with Baltimore and other regional destinations.
-
C.
UN M49
UN M49 is a United Nations standard that defines numeric codes for countries and geographical regions for statistical and analytical purposes.
-
D.
Korean MARC
Korean MARC is a national bibliographic metadata standard used in South Korea for cataloging library and information resources.
-
E.
MARC standards
chosen
MARC standards are a set of bibliographic data formats used worldwide to structure and exchange library catalog information in a consistent, machine-readable way.
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbc20b20819095fedf803aadc53a |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e5133ba48190a18ea170e3b9e1cd |
completed | March 14, 2026, 4:33 a.m. |
Created at: March 8, 2026, 3:35 p.m.