Triple
T13676021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moira O’Hara |
E327879
|
entity |
| Predicate | appearanceToWomen |
P111115
|
FINISHED |
| Object | older woman maid |
—
|
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: older woman maid | Statement: [Moira O’Hara, appearanceToWomen, older woman maid]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appearanceToWomen Context triple: [Moira O’Hara, appearanceToWomen, older woman maid]
-
A.
appearance
Indicates how something looks or seems to an observer, including its visible form, condition, or outward impression.
-
B.
adaptationAppearance
Indicates that one entity appears or is depicted in an adaptation of another entity (such as a work being represented in a derived or reinterpreted version).
-
C.
hasCharacterAppearance
Indicates that a character appears or is visually represented within a given work, scene, or context.
-
D.
characterMajorAppearance
Indicates that a character makes a significant or prominent appearance in a particular work or installment.
-
E.
viewOnWomen
Indicates a person's attitudes, beliefs, or perspectives regarding women and gender roles.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc65c04988190b675e6fb7241e53c |
completed | April 12, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69dbbe8d8d0881908d6e89954f44eed4 |
completed | April 12, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69dbc59ca1a88190a6abd3bd00554c93 |
completed | April 12, 2026, 4:17 p.m. |
Created at: April 9, 2026, 9:53 p.m.