Triple
T7024917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Married a Witch |
E162918
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Jennifer |
E47548
|
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: Jennifer | Statement: [I Married a Witch, mainCharacter, Jennifer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennifer Context triple: [I Married a Witch, mainCharacter, Jennifer]
-
A.
Jennifer
chosen
Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
-
B.
Jane
Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
-
C.
Jane
Jane was a British sealing and exploration vessel commanded by James Weddell during his early 19th-century Antarctic voyages.
-
D.
Jane
Jane is a powerful vampire in the Twilight series, known for her childlike appearance and her ability to inflict excruciating pain with her mind as a high-ranking enforcer of the Volturi.
-
E.
Jessica
Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
- 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_69c6885b26248190a857541e3d10e299 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e1fb8f0c8190b15dd7ce7ab6a8f2 |
completed | March 27, 2026, 8 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c77581e2a88190ad2ec9855772c6a5 |
completed | March 28, 2026, 6:30 a.m. |
Created at: March 27, 2026, 2:35 p.m.