Triple
T3422536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The King of Hollywood |
E72145
|
entity |
| Predicate | appliedToProfession |
P35550
|
FINISHED |
| Object | film actor |
—
|
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: film actor | Statement: [The King of Hollywood, appliedToProfession, film actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliedToProfession Context triple: [The King of Hollywood, appliedToProfession, film actor]
-
A.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
B.
recognizesProfession
Indicates that one entity acknowledges or identifies another entity’s professional role or occupation as such.
-
C.
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.
-
D.
hasRegulatedProfession
Indicates that an entity practices or is associated with a profession that is formally regulated by laws, standards, or licensing authorities.
-
E.
memberProfession
chosen
Indicates that a member or individual holds or practices a particular profession or occupation.
- 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_69ad85ad38e48190b7660c5118a35289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb95223e081908b2954769d2f46c8 |
completed | March 8, 2026, 6 p.m. |
| PD | Predicate disambiguation | batch_69adadfea024819094b41a13bc004bda |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:15 p.m.