Triple
T19885084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1st Guards Army |
E477876
|
entity |
| Predicate | subordinateTo |
P258
|
FINISHED |
| Object | Stavka |
E50411
|
NE 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: Stavka | Statement: [1st Guards Army, subordinateTo, Stavka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stavka Context triple: [1st Guards Army, subordinateTo, Stavka]
-
A.
Stavka
chosen
Stavka was the high command of the Soviet armed forces during World War II, responsible for overall strategic direction and coordination of military operations.
-
B.
Stavenisse
Stavenisse is a small village in the Dutch province of Zeeland, located on the island of Tholen and known for its dike landscapes and fishing heritage.
-
C.
Stiattesi
Stiattesi is an Italian surname, notably borne by Prudenzia Stiattesi, a historical figure from Italy.
-
D.
Stiva
Stiva is the familiar nickname of Stepan Arkadyevich Oblonsky, a charming, pleasure-loving Moscow nobleman and key supporting character in Leo Tolstoy’s novel "Anna Karenina."
-
E.
Starina
Starina is the flamboyant drag stage persona of Albert Goldman in the film "The Birdcage."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659093df081909d3be9c3caeb79ba |
completed | April 20, 2026, 4:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07dbcf6f7c8190901a78dfd4cf6b4d |
completed | May 16, 2026, 2:51 a.m. |
Created at: April 10, 2026, 1:52 p.m.