Triple
T973792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special Victims Division |
E21003
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object |
SVD
SVD is the abbreviation for the Special Victims Division, a specialized police unit that investigates sensitive crimes such as sexual offenses and crimes against vulnerable victims.
|
E115321
|
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: SVD | Statement: [Special Victims Division, hasAbbreviation, SVD]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SVD Context triple: [Special Victims Division, hasAbbreviation, SVD]
-
A.
SVR
SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
-
B.
SVR
SVR is the set of post-nominal letters used to denote recipients of the Order of the White Rose of Finland.
-
C.
SVC
SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
-
D.
SV
SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
-
E.
SD
SD is the standard abbreviation for the San Diego Padres, a Major League Baseball team based in San Diego, California.
- 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: SVD Triple: [Special Victims Division, hasAbbreviation, SVD]
Generated description
SVD is the abbreviation for the Special Victims Division, a specialized police unit that investigates sensitive crimes such as sexual offenses and crimes against vulnerable victims.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SVD Target entity description: SVD is the abbreviation for the Special Victims Division, a specialized police unit that investigates sensitive crimes such as sexual offenses and crimes against vulnerable victims.
-
A.
SVR
SVR is the set of post-nominal letters used to denote recipients of the Order of the White Rose of Finland.
-
B.
SVR
SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
-
C.
SVC
SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
-
D.
SV
SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
-
E.
SD
SD is the standard abbreviation for the San Diego Padres, a Major League Baseball team based in San Diego, California.
- 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_69a493c2b62c8190b616351789ec47f8 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b45f28f081908d41b2d7f353708d |
completed | March 1, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac170a00f481909da0394531ac24fe |
completed | March 7, 2026, 12:16 p.m. |
| NEDg | Description generation | batch_69ac18e9be2081909770ab2ead56d0db |
completed | March 7, 2026, 12:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac195b7cd08190b2c3f07d7ae849ed |
completed | March 7, 2026, 12:26 p.m. |
Created at: March 1, 2026, 7:40 p.m.