Triple
T7655650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maureen McCarthy Scalia |
E173373
|
entity |
| Predicate | givenName |
P17
|
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: [Maureen McCarthy Scalia, givenName, Maureen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maureen Context triple: [Maureen McCarthy Scalia, givenName, 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.
Marjorie
Marjorie is a feminine given name of French origin that has been widely used in English-speaking countries.
-
D.
Madelaine
Madelaine is a character in the Danish crime thriller film "The Salvation."
-
E.
Alvirah Meehan
Alvirah Meehan is an amateur sleuth and former cleaning woman turned lottery winner who appears as the sharp, nosy, and warm-hearted protagonist in Mary Higgins Clark’s mystery stories.
- 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_69c6995473348190a4f41d110d619a18 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7018ea3688190907c3ac7d25e3da6 |
completed | March 27, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89afd1438819080c8f097df1d1453 |
completed | March 29, 2026, 3:22 a.m. |
Created at: March 27, 2026, 3:59 p.m.