Triple
T14143980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maison médicale du roi |
E350495
|
entity |
| Predicate | includedProfession |
P69514
|
FINISHED |
| Object | physician |
—
|
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: physician | Statement: [Maison médicale du roi, includedProfession, physician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includedProfession Context triple: [Maison médicale du roi, includedProfession, physician]
-
A.
includesProfession
chosen
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
B.
relatedProfession
Indicates that two entities have professions that are connected or associated in some meaningful way, such as being in the same field, industry, or professional domain.
-
C.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
D.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
-
E.
recognizesProfession
Indicates that one entity acknowledges or identifies another entity’s professional role or occupation as such.
- 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_69d827865f608190b311820428ae027b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61214de081909a5186ff11336f97 |
completed | April 14, 2026, 3:45 p.m. |
| PD | Predicate disambiguation | batch_69de05b5e7a08190a16be9ad8b92b80c |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 12:52 a.m.