Triple
T214303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MARC standards |
E4784
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
KORMARC
KORMARC is the Korean implementation of the MARC bibliographic data format standard used for cataloging and exchanging library records in Korea.
|
E27070
|
NE FINISHED |
How this triple was built (4 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: KORMARC | Statement: [MARC standards, hasComponent, KORMARC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KORMARC Context triple: [MARC standards, hasComponent, KORMARC]
-
A.
HMC
HMC is a professional association of leading independent school heads in the United Kingdom and internationally.
-
B.
Kalorama
Kalorama is an affluent, historic residential neighborhood in Northwest Washington, D.C., known for its embassies, stately mansions, and prominent political residents.
-
C.
Nourse
Nourse is a surname and variant spelling of "Nurse," historically associated with English-speaking families and occasionally used as a place or business name.
-
D.
Sakai
Sakai is a major Japanese city in Osaka Prefecture known historically as a prosperous port and merchant center and today as an important industrial and cultural hub.
-
E.
IMCO
IMCO is the former acronym for the International Maritime Organization, the United Nations agency responsible for regulating international shipping and maritime safety.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: KORMARC Triple: [MARC standards, hasComponent, KORMARC]
Generated description
KORMARC is the Korean implementation of the MARC bibliographic data format standard used for cataloging and exchanging library records in Korea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KORMARC Target entity description: KORMARC is the Korean implementation of the MARC bibliographic data format standard used for cataloging and exchanging library records in Korea.
-
A.
HMC
HMC is a professional association of leading independent school heads in the United Kingdom and internationally.
-
B.
Kalorama
Kalorama is an affluent, historic residential neighborhood in Northwest Washington, D.C., known for its embassies, stately mansions, and prominent political residents.
-
C.
Nourse
Nourse is a surname and variant spelling of "Nurse," historically associated with English-speaking families and occasionally used as a place or business name.
-
D.
Sakai
Sakai is a major Japanese city in Osaka Prefecture known historically as a prosperous port and merchant center and today as an important industrial and cultural hub.
-
E.
IMCO
IMCO is the former acronym for the International Maritime Organization, the United Nations agency responsible for regulating international shipping and maritime safety.
- F. None of above. chosen
Provenance (5 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_69a338e980908190871a2236375de6bd |
completed | Feb. 28, 2026, 6:50 p.m. |
| NEDg | Description generation | batch_69a339380100819084982498f6b7161e |
completed | Feb. 28, 2026, 6:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a33959072c819087e699c8a583e020 |
completed | Feb. 28, 2026, 6:52 p.m. |
Created at: Feb. 28, 2026, 2:52 a.m.