Triple
T14393093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ACM SIGARCH |
E356892
|
entity |
| Predicate | sponsorOf |
P1807
|
FINISHED |
| Object |
MICRO
MICRO is a leading annual international conference focused on microarchitecture and advanced computer architecture research.
|
E1096512
|
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: MICRO | Statement: [ACM SIGARCH, sponsorOf, MICRO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MICRO Context triple: [ACM SIGARCH, sponsorOf, MICRO]
-
A.
MCRO
MCRO is the stock ticker symbol for Micro Focus International, a British multinational software and information technology company known for enterprise software solutions.
-
B.
MiC
MiC is the official abbreviation for Italy’s Ministry of Culture, the government body responsible for cultural heritage, arts, and related policies.
-
C.
Mini
Mini is a young Bengali girl in Rabindranath Tagore’s short story "Kabuliwala," whose innocent friendship with an Afghan fruit seller forms the emotional core of the narrative.
-
D.
Mini
Mini is a British automotive marque best known for its compact, stylish small cars that originated with the iconic Mini of the 1960s.
-
E.
MICC
MICC is an abbreviation for the Manchester International Convention Centre, a major venue in Manchester, England used for conferences, exhibitions, and large events.
- 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: MICRO Triple: [ACM SIGARCH, sponsorOf, MICRO]
Generated description
MICRO is a leading annual international conference focused on microarchitecture and advanced computer architecture research.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MICRO Target entity description: MICRO is a leading annual international conference focused on microarchitecture and advanced computer architecture research.
-
A.
MCRO
MCRO is the stock ticker symbol for Micro Focus International, a British multinational software and information technology company known for enterprise software solutions.
-
B.
MiC
MiC is the official abbreviation for Italy’s Ministry of Culture, the government body responsible for cultural heritage, arts, and related policies.
-
C.
Mini
Mini is a young Bengali girl in Rabindranath Tagore’s short story "Kabuliwala," whose innocent friendship with an Afghan fruit seller forms the emotional core of the narrative.
-
D.
Mini
Mini is a British automotive marque best known for its compact, stylish small cars that originated with the iconic Mini of the 1960s.
-
E.
MICC
MICC is an abbreviation for the Manchester International Convention Centre, a major venue in Manchester, England used for conferences, exhibitions, and large events.
- 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_69de902b9acc8190817ffa848a76a880 |
completed | April 14, 2026, 7:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd551b006c8190b84449f2e2b59b62 |
completed | May 8, 2026, 3:14 a.m. |
| NEDg | Description generation | batch_69fd55d90ed08190b6a0184715f39ff4 |
completed | May 8, 2026, 3:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd565d32fc8190acc1e733537a23cb |
completed | May 8, 2026, 3:19 a.m. |
Created at: April 10, 2026, 1:16 a.m.