Triple
T5342267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer Connelly |
E123970
|
entity |
| Predicate | hasGivenName |
P17
|
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: [Jennifer Connelly, hasGivenName, Jennifer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennifer Context triple: [Jennifer Connelly, hasGivenName, 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 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.
-
D.
Jessica
Jessica Barth is an American actress best known for her comedic role as Tami-Lynn in the "Ted" film series.
-
E.
Jessica
Jessica is a women's fashion and apparel brand that was sold exclusively through Sears Canada.
- 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_69bd464b07f8819095aa76577c9829e4 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85cc5a9881909e23bf9c5b697a8e |
completed | March 20, 2026, 5:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf18cc387c8190a9fe430fe5bb38ce |
completed | March 21, 2026, 10:16 p.m. |
Created at: March 20, 2026, 2:01 p.m.