Triple
T2179933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martha |
E49016
|
entity |
| Predicate | hasCognate |
P2525
|
FINISHED |
| Object | Marta |
E243815
|
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: Marta | Statement: [Martha, hasCognate, Marta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marta Context triple: [Martha, hasCognate, Marta]
-
A.
Marta
chosen
Marta is a feminine given name commonly used in many European and Latin American countries, often considered a variant of the name Martha.
-
B.
María
"María" is a film featuring actress Taryn Power in a significant role.
-
C.
María
María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
-
D.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
-
E.
Magda
Magda is a feminine given name, commonly used as a short form of Magdalena in various European languages.
- 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_69a88aa72d348190a9544bb5b8a4e71d |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abbef0e2f0819080ca457fe3b8b419 |
completed | March 7, 2026, 6 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6af200948190a2d8866946012de4 |
completed | March 9, 2026, 6:38 a.m. |
Created at: March 4, 2026, 7:45 p.m.