Triple
T16291659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kane family |
E395537
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object | Mona Kane |
E1079065
|
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: Mona Kane | Statement: [Kane family, hasMember, Mona Kane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mona Kane Context triple: [Kane family, hasMember, Mona Kane]
-
A.
Marilyn Vance
Marilyn Vance is an American costume designer known for her influential work on numerous popular films, including iconic 1980s and 1990s movies.
-
B.
Beth Kane
chosen
Beth Kane is a DC Comics character best known as the twin sister and tragic nemesis of Batwoman, often appearing under the villainous persona Alice.
-
C.
Julie Monroe
Julie Monroe is a film editor known for her work on major motion pictures, including the financial drama "Wall Street: Money Never Sleeps."
-
D.
Vina Wray
Vina Wray is an alternate name for Fay Wray, the Canadian-American actress best known for her iconic role in the 1933 film "King Kong."
-
E.
Maxene Reynolds
Maxene Reynolds is the daughter of legendary American actress and singer Debbie Reynolds.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24919345881909ba4e7fe2e59340f |
completed | April 17, 2026, 2:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c4ab62881909c311bdc44068dc4 |
completed | May 10, 2026, 8:05 a.m. |
Created at: April 10, 2026, 5:05 a.m.