Triple
T14080489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stigmata |
E338851
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Nia Long |
E372699
|
NE 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: Nia Long | Statement: [Stigmata, portrayedBy, Nia Long]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nia Long Context triple: [Stigmata, portrayedBy, Nia Long]
-
A.
Nia Long
chosen
Nia Long is an American actress known for her roles in films like "Boyz n the Hood," "Love Jones," and "The Best Man," as well as the TV series "The Fresh Prince of Bel-Air."
-
B.
Nicole Ari Parker
Nicole Ari Parker is an American actress known for her roles in film and television, including prominent performances in romantic comedies and dramas.
-
C.
Sharley Hudson
Sharley Hudson was the wife of American character actor Keenan Wynn.
-
D.
Nicolette Robinson
Nicolette Robinson is an American actress and singer known for her work on television and Broadway, including starring in the musical "Waitress."
-
E.
Genie Francis
Genie Francis is an American actress best known for her iconic role as Laura Spencer on the long-running soap opera "General Hospital."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d81c687b0c819087fd9ed4198403f8 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5c5f759c81909bfd60ab35b0937b |
completed | April 14, 2026, 3:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcb672c08081908e1ff9030745776a |
completed | May 7, 2026, 3:57 p.m. |
Created at: April 9, 2026, 10:21 p.m.