Triple
T10109161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kirov-class battlecruiser |
E218196
|
entity |
| Predicate | shipInClass |
P3141
|
FINISHED |
| Object | Frunze |
E168938
|
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: Frunze | Statement: [Kirov-class battlecruiser, shipInClass, Frunze]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frunze Context triple: [Kirov-class battlecruiser, shipInClass, Frunze]
-
A.
Frunze
chosen
Frunze is a surname most notably associated with Mikhail Frunze, a prominent Bolshevik leader and Red Army commander during the Russian Civil War.
-
B.
Lyova
Lyova is a Russian diminutive form of the male given name Lev.
-
C.
Yunaska
Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
-
D.
Frosta
Frosta is a rural municipality and peninsula in Trøndelag county, Norway, known for its fertile farmland and historical significance as a medieval assembly site.
-
E.
Tsitska
Tsitska is a Georgian white grape variety from the Imereti region, known for producing fresh, high-acidity wines often used in both still and sparkling styles.
- 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_69ca83da93fc8190b54e44bc2b34857c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cdd0cbd8a48190b2af6177d1249f58 |
completed | April 2, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2cc0f48808190855a7f8e839ccdae |
completed | April 5, 2026, 8:54 p.m. |
Created at: March 30, 2026, 9:03 p.m.