Triple
T10608280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lieutenant Marta Robbins |
E275934
|
entity |
| Predicate | givenName |
P17
|
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: [Lieutenant Marta Robbins, givenName, Marta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marta Context triple: [Lieutenant Marta Robbins, givenName, 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.
Marta
Marta is a legendary Brazilian footballer widely regarded as one of the greatest women’s players of all time.
-
C.
Marta
Marta is a small Italian town in the Lazio region, situated on the southern shore of Lake Bolsena and known for its lakeside scenery and historic center.
-
D.
María
"María" is a film featuring actress Taryn Power in a significant role.
-
E.
María
María is a feminine given name of Hebrew origin, widely used in Spanish-speaking countries and associated with numerous historical and religious figures.
- 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_69d6aaf948d88190806cc3a8c47a3fb2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d6df4c38c881908f69bb757b8e03f5 |
completed | April 8, 2026, 11:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d97a23e24881909afb009baa0ef662 |
completed | April 10, 2026, 10:31 p.m. |
Created at: April 8, 2026, 7:32 p.m.