Triple
T2559888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Svan language |
E57216
|
entity |
| Predicate | hasISO6393Code |
P8719
|
FINISHED |
| Object |
sva
sva is the ISO 639-3 code for the Svan language, a Kartvelian language spoken by the Svan people in the Svaneti region of northwestern Georgia.
|
E278113
|
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: sva | Statement: [Svan language, hasISO6393Code, sva]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: sva Context triple: [Svan language, hasISO6393Code, sva]
-
A.
SVA
SVA is the ICAO airline designator used to identify Saudia, the flag carrier airline of Saudi Arabia, in international aviation operations.
-
B.
SV
SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
-
C.
SV
SV is the commonly used abbreviation for the Faculty of Social Sciences at the University of Oslo, encompassing disciplines such as sociology, political science, economics, and related fields.
-
D.
SVC
SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
-
E.
SVR
SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
- 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: sva Triple: [Svan language, hasISO6393Code, sva]
Generated description
sva is the ISO 639-3 code for the Svan language, a Kartvelian language spoken by the Svan people in the Svaneti region of northwestern Georgia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: sva Target entity description: sva is the ISO 639-3 code for the Svan language, a Kartvelian language spoken by the Svan people in the Svaneti region of northwestern Georgia.
-
A.
SVA
SVA is the ICAO airline designator used to identify Saudia, the flag carrier airline of Saudi Arabia, in international aviation operations.
-
B.
SV
SV is the two-letter ISO 3166-1 alpha-2 country code assigned to El Salvador.
-
C.
SV
SV is the commonly used abbreviation for the Faculty of Social Sciences at the University of Oslo, encompassing disciplines such as sociology, political science, economics, and related fields.
-
D.
SVC
SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
-
E.
SVR
SVR is Russia’s primary foreign intelligence service, which succeeded the Soviet-era KGB’s external intelligence functions after the USSR’s dissolution.
- 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_69ab4a4ef9008190a0e6d4422b9418b7 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd333370c8190b1d64ec99e999913 |
completed | March 7, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af5d233dbc81909feb1127cffb027f |
completed | March 9, 2026, 11:52 p.m. |
| NEDg | Description generation | batch_69af60e78b488190bdd01ee77ed3648b |
completed | March 10, 2026, 12:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69af614ad2408190955c430cb7a7e302 |
completed | March 10, 2026, 12:09 a.m. |
Created at: March 6, 2026, 9:48 p.m.