Triple
T31252684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara Hershey as Mary Magdalene |
E796871
|
entity |
| Predicate | subjectOfDiscussion |
P165591
|
FINISHED |
| Object | interpretations of Mary Magdalene in film |
—
|
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: interpretations of Mary Magdalene in film | Statement: [Barbara Hershey as Mary Magdalene, subjectOfDiscussion, interpretations of Mary Magdalene in film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectOfDiscussion Context triple: [Barbara Hershey as Mary Magdalene, subjectOfDiscussion, interpretations of Mary Magdalene in film]
-
A.
topicOfDiscourse
chosen
Indicates that something serves as the subject or focus of a particular discussion, conversation, or communicative act.
-
B.
topicOfDialogue
Indicates that a particular subject or theme is the main focus of a dialogue or conversation between entities.
-
C.
frequentlyDiscussedIn
Indicates that a topic, subject, or entity is often the focus of conversation, debate, or mention within a particular context or medium.
-
D.
subjectOfMessages
Indicates that an entity is the main topic or focus about which the messages are written or communicated.
-
E.
subjectOfEvent
Indicates that an entity participates in or is involved in a particular event as one of its primary actors or focal points.
- 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_69f224dc84d0819081f1cb6f9127e6b1 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8cec6d48190bebfa884b2f938c0 |
completed | May 3, 2026, 7:58 p.m. |
Created at: April 29, 2026, 9:12 p.m.