Triple
T11747841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maura |
E279329
|
entity |
| Predicate | nameVariantOf |
P39
|
FINISHED |
| Object | Maureen |
E213961
|
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: Maureen | Statement: [Maura, nameVariantOf, Maureen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maureen Context triple: [Maura, nameVariantOf, Maureen]
-
A.
Maureen
chosen
Maureen is a feminine given name of Irish origin, commonly used in English-speaking countries.
-
B.
Maryanne
Maryanne is a feminine given name, often used in English-speaking countries as a variant of Mary Ann or Marianne.
-
C.
Maeve Wiley
Maeve Wiley is a sharp, rebellious, and intellectually gifted teenager from the series "Sex Education," known for her complex personal life and partnership with Otis Milburn in running an underground sex therapy clinic at their school.
-
D.
Marjorie
Marjorie is a feminine given name of French origin that has been widely used in English-speaking countries.
-
E.
Marjorie
"Marjorie" is a reflective, emotionally intimate song by Taylor Swift from her album *Evermore*, written as a tribute to her late grandmother.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a50763a081908597da118bd0a64e |
completed | April 10, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f01a0492b48190b6f2e3cf36b4f537 |
completed | April 28, 2026, 2:23 a.m. |
Created at: April 8, 2026, 9:41 p.m.