Triple
T30902711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SNMPv2-SMI |
E787213
|
entity |
| Predicate | definesMacro |
P194366
|
FINISHED |
| Object | MODULE-IDENTITY |
—
|
LITERAL 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: MODULE-IDENTITY | Statement: [SNMPv2-SMI, definesMacro, MODULE-IDENTITY]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: definesMacro Context triple: [SNMPv2-SMI, definesMacro, MODULE-IDENTITY]
-
A.
definesMAC
Indicates that one entity specifies or establishes the Media Access Control (MAC) address or MAC-related configuration for another entity.
-
B.
definesSyntax
Indicates that one entity specifies or determines the formal structure, rules, or grammar by which another entity is expressed or interpreted.
-
C.
usesMacroLanguage
Indicates that one entity communicates or operates using a broader macro language that encompasses or organizes other related languages or language varieties.
-
D.
definesSymbol
Indicates that one entity specifies or establishes another entity as a symbol representing some concept, value, or object.
-
E.
hasMacroLanguage
Indicates that one language functions as a macrolanguage encompassing or grouping together multiple closely related individual languages or varieties.
- F. None of above. chosen
Provenance (4 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_69f224bcbcb48190836df847424e4057 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd6dbd1b648190b1a0b391c03aebc5 |
completed | May 8, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69fd6a9020548190bbfa845360ac85fb |
completed | May 8, 2026, 4:46 a.m. |
| PDg | Predicate description generation | batch_69fd6dbc3ac0819093fbcfe95f12b93d |
completed | May 8, 2026, 4:59 a.m. |
Created at: April 29, 2026, 8:50 p.m.