Triple
T31957125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Melissa & Joey |
E815935
|
entity |
| Predicate | leadFemaleRoleType |
P173057
|
FINISHED |
| Object | guardian of niece and nephew |
—
|
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: guardian of niece and nephew | Statement: [Melissa & Joey, leadFemaleRoleType, guardian of niece and nephew]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadFemaleRoleType Context triple: [Melissa & Joey, leadFemaleRoleType, guardian of niece and nephew]
-
A.
leadActress
Indicates that the subject is the primary female performer in the specified film, show, or production.
-
B.
leadRoleActor
Indicates that an actor performs a leading or principal role in a work or production.
-
C.
leadCharacterCaste
Indicates that the lead character in a work belongs to a specified caste.
-
D.
femaleRole
Indicates that the role, function, or position involved is associated with or designated as female.
-
E.
leadActressCharacterName
Indicates the name of the character portrayed by the lead actress in a given work.
- 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_69f348f4ec708190abbb2a7c3ed58844 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b2d9aad88190a445f8f591cb19fc |
completed | May 3, 2026, 2:28 a.m. |
| PD | Predicate disambiguation | batch_69f6b151ad008190836c1bcdec503ce2 |
completed | May 3, 2026, 2:22 a.m. |
| PDg | Predicate description generation | batch_69f6b21da77081908c5c015c4606d344 |
completed | May 3, 2026, 2:25 a.m. |
Created at: May 1, 2026, 12:08 a.m.