Triple
T38096165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ani Papyrus |
E951248
|
entity |
| Predicate | depictsOccupationOfBeneficiary |
P39104
|
FINISHED |
| Object | scribe |
—
|
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: scribe | Statement: [Ani Papyrus, depictsOccupationOfBeneficiary, scribe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: depictsOccupationOfBeneficiary Context triple: [Ani Papyrus, depictsOccupationOfBeneficiary, scribe]
-
A.
beneficiaryRole
Indicates that one entity serves as the beneficiary or recipient of an action, service, or resource provided by another entity.
-
B.
beneficiaryType
Indicates the type or category of beneficiary that receives or is intended to receive the benefit or outcome of an action or resource.
-
C.
notableBeneficialOwnerOccupation
Indicates the occupation or professional role held by a person who is a notable beneficial owner of an entity.
-
D.
recipientOccupation
chosen
Indicates that the object specifies the job, profession, or role held by the recipient in the described relationship or event.
-
E.
beneficiaryBody
Indicates that one entity serves as the benefiting body or organization that receives advantage, support, or services from another entity or action.
- 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_69f76f04960c8190a83f14ae4c67f5bc |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fdd2be648c8190b60b3d1caeb44364 |
completed | May 8, 2026, 12:10 p.m. |
| PD | Predicate disambiguation | batch_69fdd14a5c708190a6f95ec61f4fc28f |
completed | May 8, 2026, 12:04 p.m. |
Created at: May 3, 2026, 4:21 p.m.