Triple
T2179928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martha |
E49016
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Marti |
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: Marti | Statement: [Martha, hasDiminutive, Marti]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marti Context triple: [Martha, hasDiminutive, Marti]
-
A.
Martz
Martz is a surname most notably associated with Mike Martz, an American football coach known for his innovative offensive strategies in the NFL.
-
B.
Martie
chosen
Martie is a diminutive given name, typically used as a familiar or affectionate form of the name Martha.
-
C.
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
-
D.
Mimi
Mimi is a common affectionate diminutive or nickname for the given name Marie.
-
E.
Mara
Mara is a surname of Irish origin borne by various notable individuals in fields such as sports, entertainment, and politics.
- 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_69ae653de18481909c3521e060540a38 |
completed | March 9, 2026, 6:14 a.m. |
Created at: March 4, 2026, 7:45 p.m.