Triple
T10793310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martha-Ann Alito |
E254637
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Martha-Ann |
E241288
|
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: Martha-Ann | Statement: [Martha-Ann Alito, givenName, Martha-Ann]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martha-Ann Context triple: [Martha-Ann Alito, givenName, Martha-Ann]
-
A.
Arletta
Arletta, better known as Herleva of Falaise, was the mother of William the Conqueror and a notable figure in 11th-century Norman history.
-
B.
Martie
chosen
Martie is a diminutive given name, typically used as a familiar or affectionate form of the name Martha.
-
C.
Anna Molly
"Anna Molly" is a 2006 alternative rock song by the American band Incubus, known for its energetic sound and enigmatic, wordplay-based title.
-
D.
Angela Travers
Angela Travers is a spirited and impulsive young woman in P. G. Wodehouse’s Jeeves stories, notably entangled in romantic and comedic mishaps that Jeeves must help untangle.
-
E.
Marjorie
Marjorie is a feminine given name of French origin that has been widely used in English-speaking countries.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d732f878648190be5e25c56a7511cf |
completed | April 9, 2026, 5:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de564748ac8190beaaea44bb2d95ed |
completed | April 14, 2026, 2:59 p.m. |
Created at: April 8, 2026, 9:17 p.m.