Triple
T30791610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martínez Ocasio |
E784111
|
entity |
| Predicate | nameBearerProfession |
P170143
|
FINISHED |
| Object | rapper |
—
|
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: rapper | Statement: [Martínez Ocasio, nameBearerProfession, rapper]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nameBearerProfession Context triple: [Martínez Ocasio, nameBearerProfession, rapper]
-
A.
nameBearerType
Indicates the specific role or capacity in which an entity bears or carries a given name (e.g., as a person, place, organization, or other type of name bearer).
-
B.
namesakeOccupation
Indicates that one entity’s occupation is the same as, or derived from, the occupation associated with the other entity’s namesake.
-
C.
nameBearerFieldOfWork
Indicates that the field of work is associated with or characterized by the person or entity whose name it bears.
-
D.
namedAfterOccupationOrRole
Indicates that an entity is named after a specific occupation, profession, or social role associated with a person or group.
-
E.
commonProfessionAmongBearers
Indicates that multiple entities sharing a given attribute (such as a name or title) are frequently associated with the same profession.
- 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_69f224b2e2a48190b19aa43db9da5b67 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6900dbd908190baf39dd5cf37d619 |
completed | May 3, 2026, midnight |
| PD | Predicate disambiguation | batch_69f686140aa08190a35f62572b2db9b6 |
completed | May 2, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69f68848ad348190a2fb6e841dcfdb7d |
completed | May 2, 2026, 11:27 p.m. |
Created at: April 29, 2026, 8:42 p.m.