Triple
T13573576
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KLM line |
E324224
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Makarov |
E436841
|
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: Makarov | Statement: [KLM line, namedAfter, Makarov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Makarov Context triple: [KLM line, namedAfter, Makarov]
-
A.
Makarov
chosen
Makarov is a Russian surname most prominently associated with notable figures such as Hall of Fame ice hockey player Sergei Makarov.
-
B.
Fedor Tokarev
Fedor Tokarev was a prominent Soviet firearms designer best known for creating influential weapons such as the Tokarev pistol and the SVT-40 semi-automatic rifle.
-
C.
Fedorov
Fedorov is a common Russian surname borne by numerous notable figures in fields such as sports, science, and the arts.
-
D.
Vassili Zaitsev
Vassili Zaitsev is a legendary Soviet sniper of World War II, renowned for his exploits during the Battle of Stalingrad and later popularized in film and literature.
-
E.
Pavlichenko
Pavlichenko is a Ukrainian surname most famously borne by Lyudmila Pavlichenko, a celebrated Soviet World War II sniper.
- 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_69d80769100c819099111274614f5ed2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0106cb48190b20eb9bda131a68a |
completed | April 12, 2026, 2:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f76bb827d48190958e5710d554cd04 |
completed | May 3, 2026, 3:37 p.m. |
Created at: April 9, 2026, 9:48 p.m.