Triple
T2852006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leonhard |
E63113
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Leonid |
E145654
|
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: Leonid | Statement: [Leonhard, relatedName, Leonid]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leonid Context triple: [Leonhard, relatedName, Leonid]
-
A.
Leonid
chosen
Leonid is a masculine given name of Slavic origin, notably borne by Soviet leader Leonid Brezhnev.
-
B.
Semyon
Semyon is a masculine given name of Russian origin, commonly used in Slavic countries.
-
C.
Nikolay
Nikolay is a masculine given name of Slavic origin, commonly used in Russian-speaking countries and equivalent to Nicholas in English.
-
D.
Yevgeny
Yevgeny is a masculine given name of Slavic origin, commonly used in Russian-speaking countries.
-
E.
Sergei
Sergei is a masculine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
- 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_69ab4c407c408190857d25e027155ce9 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf5ca2648190bd32c6ec4b0dd3b6 |
completed | March 7, 2026, 8:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b62788456c8190a484826e5d915d9a |
completed | March 15, 2026, 3:29 a.m. |
Created at: March 6, 2026, 10:02 p.m.