Triple
T19927701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan Mayer |
E478969
|
entity |
| Predicate | hasDaughter |
P24357
|
FINISHED |
| Object | Julie Mayer |
—
|
NE NERFINISHED |
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: Julie Mayer | Statement: [Susan Mayer, hasDaughter, Julie Mayer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julie Mayer Context triple: [Susan Mayer, hasDaughter, Julie Mayer]
-
A.
Julie Mayer
chosen
Julie Mayer is a fictional character from the television series "Desperate Housewives," known as Susan Mayer's intelligent and responsible daughter.
-
B.
Julie Grau
Julie Grau is an American book editor and publisher best known for co-founding and leading influential imprints such as Spiegel & Grau.
-
C.
Julie Weiss
Julie Weiss is an acclaimed American costume designer known for her work in film, television, and theater, including multiple Academy Award–nominated productions.
-
D.
Julie Durk
Julie Durk is a film producer best known for her work on the romantic comedy "You've Got Mail."
-
E.
Julie Sussman
Julie Sussman is a computer scientist and author best known for coauthoring the influential textbook "Structure and Interpretation of Computer Programs" and contributing to the development of programming language education.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659cc1a448190aa98d4a66022457e |
completed | April 20, 2026, 4:52 p.m. |
Created at: April 10, 2026, 1:53 p.m.