Triple
T9860215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mendeleyevskaya |
E239689
|
entity |
| Predicate | namedForProfession |
P41835
|
FINISHED |
| Object | chemist |
—
|
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: chemist | Statement: [Mendeleyevskaya, namedForProfession, chemist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: namedForProfession Context triple: [Mendeleyevskaya, namedForProfession, chemist]
-
A.
namedPersonOccupation
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
B.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
C.
occupationalNameFor
Indicates that one entity is the name or label used to denote the occupation or profession of another entity.
-
D.
isNamedAfterOccupation
chosen
Indicates that an entity’s name is derived from or based on a particular occupation or profession.
-
E.
namedForWork
Indicates that one entity is named in honor of, or derived from the title of, a particular work (such as a book, film, artwork, or other creative production).
- 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_69ca84e6493081909cf58c8d42ea856b |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3b582cc81909a6d638fe2573c43 |
completed | April 2, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69cd1d7621d48190aa6a6f34399514b0 |
completed | April 1, 2026, 1:28 p.m. |
Created at: March 30, 2026, 8:35 p.m.