Triple
T30672967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thony De La Rosa |
E780840
|
entity |
| Predicate | professionBeforeSeries |
P35945
|
FINISHED |
| Object | 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: surgeon | Statement: [Thony De La Rosa, professionBeforeSeries, surgeon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionBeforeSeries Context triple: [Thony De La Rosa, professionBeforeSeries, surgeon]
-
A.
portrayedProfessionOfCharacter
Indicates that one entity is the profession or occupation depicted as being held by a particular character.
-
B.
portrayedByProfession
Indicates that an entity is depicted or represented by someone acting in a specified professional capacity.
-
C.
characterFormerOccupation
chosen
Indicates that a character previously held a specific occupation but no longer does.
-
D.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
E.
starOccupationInSeries
Indicates that an individual has a specific occupation or role as a starring character within a particular series.
- 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_69f224a7fc208190a07d6d3879b31640 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6a0ea04888190ac3a813b603bcb5c |
completed | May 3, 2026, 1:12 a.m. |
| PD | Predicate disambiguation | batch_69f69fe463248190aa78128abeab1183 |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 29, 2026, 8:32 p.m.