Triple
T15114149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Voter Education Project |
E360991
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object |
VEP
VEP is an acronym commonly used for the Voter Education Project, a civil rights-era initiative that supported voter registration and political participation among African Americans in the U.S. South.
|
E1139257
|
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: VEP | Statement: [Voter Education Project, hasAlternativeName, VEP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: VEP Context triple: [Voter Education Project, hasAlternativeName, VEP]
-
A.
VEPT
VEPT is the ICAO airport code for Jay Prakash Narayan International Airport in Patna, India.
-
B.
VEPA
VEPA (Virtual Ethernet Port Aggregator) is a networking mechanism that forwards virtual machine traffic to an external switch for centralized policy enforcement and management.
-
C.
VEF
VEF was the ISO 4217 currency code for the Venezuelan bolívar used before its redenomination to the bolívar soberano.
-
D.
VE
VE is the Italian vehicle registration code assigned to the Metropolitan City of Venice.
-
E.
VE
VE is the two-letter ISO 3166-1 alpha-2 country code assigned to Venezuela for international standardization and identification purposes.
- 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: VEP Triple: [Voter Education Project, hasAlternativeName, VEP]
Generated description
VEP is an acronym commonly used for the Voter Education Project, a civil rights-era initiative that supported voter registration and political participation among African Americans in the U.S. South.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: VEP Target entity description: VEP is an acronym commonly used for the Voter Education Project, a civil rights-era initiative that supported voter registration and political participation among African Americans in the U.S. South.
-
A.
VEPT
VEPT is the ICAO airport code for Jay Prakash Narayan International Airport in Patna, India.
-
B.
VEPA
VEPA (Virtual Ethernet Port Aggregator) is a networking mechanism that forwards virtual machine traffic to an external switch for centralized policy enforcement and management.
-
C.
VEF
VEF was the ISO 4217 currency code for the Venezuelan bolívar used before its redenomination to the bolívar soberano.
-
D.
VE
VE is the Italian vehicle registration code assigned to the Metropolitan City of Venice.
-
E.
VE
VE is the two-letter ISO 3166-1 alpha-2 country code assigned to Venezuela for international standardization and identification purposes.
- 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_69d85a0491ec8190830960be8fafb994 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0058f4fb88190a3d446a466aebcf1 |
completed | April 15, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feb7eccb988190aae27dd28cf50997 |
completed | May 9, 2026, 4:28 a.m. |
| NEDg | Description generation | batch_69febc7e6d408190983a1f1e706c67b1 |
completed | May 9, 2026, 4:47 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69febd10a9a08190aabfa072199db5c9 |
completed | May 9, 2026, 4:50 a.m. |
Created at: April 10, 2026, 3:05 a.m.