Triple
T35224104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsiolkovskiy E |
E1017041
|
entity |
| Predicate | eponymDerivedFrom |
P131286
|
FINISHED |
| Object | Konstantin Tsiolkovsky |
—
|
NE NERFINISHED |
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: Konstantin Tsiolkovsky | Statement: [Tsiolkovskiy E, eponymDerivedFrom, Konstantin Tsiolkovsky]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eponymDerivedFrom Context triple: [Tsiolkovskiy E, eponymDerivedFrom, Konstantin Tsiolkovsky]
-
A.
eponymFor
Indicates that one entity gives its name to another entity, which is then named after it.
-
B.
eponymOriginCountry
Indicates the country from which the person or entity that gave its name (as an eponym) to something originates.
-
C.
eponymIsFrom
chosen
Indicates that the name of one entity is derived from or named after another entity.
-
D.
eponymProfession
Indicates that a person’s profession is the source of an eponym, i.e., a word or name derived from that professional role.
-
E.
eponymBirthPlace
Indicates the place where the person after whom something is named was born.
- F. None of above.
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_69f76de072908190ab65038a8a7b6a79 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79533b88c8190934ec4cb21770e24 |
completed | May 3, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69f79104f5b48190a496cdffde8472da |
completed | May 3, 2026, 6:16 p.m. |
Created at: May 3, 2026, 4:02 p.m.