Triple
T38365189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Supporting Actress for "None But the Lonely Heart" |
E892418
|
entity |
| Predicate | recipientProfession |
P39104
|
FINISHED |
| Object | actress |
—
|
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: actress | Statement: [Academy Award for Best Supporting Actress for "None But the Lonely Heart", recipientProfession, actress]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recipientProfession Context triple: [Academy Award for Best Supporting Actress for "None But the Lonely Heart", recipientProfession, actress]
-
A.
recipientOccupation
chosen
Indicates that the object specifies the job, profession, or role held by the recipient in the described relationship or event.
-
B.
ownerProfession
Indicates that the profession or occupation is associated with, or held by, the owner of a specified entity.
-
C.
recipientWork
Indicates that one work is the item or creative work received by an agent or entity in the context of a transfer, award, or similar event.
-
D.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
E.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
- 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_69f76e47cb4c8190bdd92cd1db59c0c5 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcc7a4d7f881908b43b960911b81e9 |
completed | May 7, 2026, 5:11 p.m. |
| PD | Predicate disambiguation | batch_69fcc589720c819089c8f500fea3c86a |
completed | May 7, 2026, 5:02 p.m. |
Created at: May 3, 2026, 4:31 p.m.