Triple
T12670623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Michael Hfuhruhurr |
E302667
|
entity |
| Predicate | hasProfessionTrait |
P106209
|
FINISHED |
| Object | highly skilled surgeon |
—
|
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: highly skilled surgeon | Statement: [Dr. Michael Hfuhruhurr, hasProfessionTrait, highly skilled surgeon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionTrait Context triple: [Dr. Michael Hfuhruhurr, hasProfessionTrait, highly skilled surgeon]
-
A.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
B.
hasInterpreterProfession
Indicates that an entity works in the professional role or occupation of an interpreter.
-
C.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
D.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
E.
recognizesProfession
Indicates that one entity acknowledges or identifies another entity’s professional role or occupation as such.
- F. None of above. chosen
Provenance (4 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960bb64ec8190bd0400cf0cc8b0a7 |
completed | April 10, 2026, 8:42 p.m. |
| PDg | Predicate description generation | batch_69d961acadb8819098de743bc951fedb |
completed | April 10, 2026, 8:46 p.m. |
Created at: April 9, 2026, 5:20 p.m.