Triple
T15161484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mikhaylovich |
E362226
|
entity |
| Predicate | precedesInFullName |
P11124
|
FINISHED |
| Object | family name |
—
|
LITERAL 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: family name | Statement: [Mikhaylovich, precedesInFullName, family name]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: precedesInFullName Context triple: [Mikhaylovich, precedesInFullName, family name]
-
A.
precedesLetter
Indicates that one letter comes immediately before another letter in a specified ordering, such as the alphabet or a given sequence.
-
B.
predecessorName
Indicates that the value is the name of an entity that directly precedes another in an ordered sequence or lineage.
-
C.
appearsBefore
chosen
Indicates that one entity occurs, is positioned, or is presented earlier in an ordered sequence than another entity.
-
D.
precededByOfficeTitle
Indicates that one office title directly came before another in an ordered sequence of office positions.
-
E.
hasPredecessorNameInCity
Indicates that an entity has a predecessor (e.g., an earlier version or prior holder) that had the same name in a specified city.
- 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0060f2efc8190aa0eb5fb8d4ce085 |
completed | April 15, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69deb9779acc81908ed2dad382c42dca |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:08 a.m.