Triple
T11029118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Intimate Apparel |
E260711
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Mayme |
E671613
|
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: Mayme | Statement: [Intimate Apparel, hasCharacter, Mayme]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mayme Context triple: [Intimate Apparel, hasCharacter, Mayme]
-
A.
Mayme Kelso
chosen
Mayme Kelso was an American actress of the silent film era, known for her character roles in early 20th-century cinema.
-
B.
Mary Lou
Mary Lou is a technology innovator and entrepreneur best known for her pioneering work in display and imaging technologies, including co-founding One Laptop per Child and founding Openwater.
-
C.
Marjorie
Marjorie is a feminine given name of French origin that has been widely used in English-speaking countries.
-
D.
Marjorie
"Marjorie" is a reflective, emotionally intimate song by Taylor Swift from her album *Evermore*, written as a tribute to her late grandmother.
-
E.
Gladys
Gladys is a feminine given name of English origin that was especially popular in the late 19th and early 20th centuries.
- 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797d2feb881909a5684721e8b0d9c |
completed | April 9, 2026, 12:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3753451e08190bc42ab99d01926f9 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 8, 2026, 9:25 p.m.