Triple
T14401608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Estonian Ministry of Education and Research |
E357084
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
HM
HM is the official abbreviation for Estonia’s Ministry of Education and Research, the government body responsible for national education and research policy.
|
E1097356
|
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: HM | Statement: [Estonian Ministry of Education and Research, abbreviation, HM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HM Context triple: [Estonian Ministry of Education and Research, abbreviation, HM]
-
A.
HM
HM is an abbreviation commonly used as a formal title for a reigning queen or king, standing for "Her Majesty" or "His Majesty."
-
B.
HM
HM is the abbreviated designation commonly used for Hungary’s Ministry of Defence.
-
C.
HM
HM is a prefix used for classes and APIs in Apple's HomeKit framework, which enables communication and control of smart home accessories on iOS and other Apple platforms.
-
D.
MH
MH is the two-letter ISO 3166-1 alpha-2 country code representing the Republic of the Marshall Islands.
-
E.
MH
MH is the two-letter IATA airline designator used to identify Malaysia Airlines on tickets, timetables, and flight numbers.
- 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: HM Triple: [Estonian Ministry of Education and Research, abbreviation, HM]
Generated description
HM is the official abbreviation for Estonia’s Ministry of Education and Research, the government body responsible for national education and research policy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HM Target entity description: HM is the official abbreviation for Estonia’s Ministry of Education and Research, the government body responsible for national education and research policy.
-
A.
HM
HM is an abbreviation commonly used as a formal title for a reigning queen or king, standing for "Her Majesty" or "His Majesty."
-
B.
HM
HM is a prefix used for classes and APIs in Apple's HomeKit framework, which enables communication and control of smart home accessories on iOS and other Apple platforms.
-
C.
HM
HM is the abbreviated designation commonly used for Hungary’s Ministry of Defence.
-
D.
MH
MH is the two-letter ISO 3166-1 alpha-2 country code representing the Republic of the Marshall Islands.
-
E.
MH
MH is the two-letter IATA airline designator used to identify Malaysia Airlines on tickets, timetables, and flight numbers.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de908500048190bb6a20fe318d5c62 |
completed | April 14, 2026, 7:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5520c07c8190bfdaf224dd779ced |
completed | May 8, 2026, 3:14 a.m. |
| NEDg | Description generation | batch_69fd56bbd6e481909fd97f3808bc99fd |
completed | May 8, 2026, 3:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd5755156c8190bc27df83e940c403 |
completed | May 8, 2026, 3:24 a.m. |
Created at: April 10, 2026, 1:17 a.m.