Triple
T8817922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marinus |
E209827
|
entity |
| Predicate | variantForm |
P4680
|
FINISHED |
| Object | Marino |
E599827
|
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: Marino | Statement: [Marinus, variantForm, Marino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marino Context triple: [Marinus, variantForm, Marino]
-
A.
Marino
Marino is a surname most famously associated with Dan Marino, the Hall of Fame former NFL quarterback for the Miami Dolphins.
-
B.
Marino
chosen
Marino is a historic town in Italy’s Alban Hills near Rome, known for its wine production and annual grape festival.
-
C.
Molinaro
Molinaro is an Italian occupational surname, historically associated with millers and derived from the same root as "Molinero."
-
D.
Vannino
Vannino is an Italian diminutive given name derived from Giovanni, commonly used as an affectionate or familiar form.
-
E.
Martino
Martino is a surname most prominently associated with Argentine football manager and former player Gerardo "Tata" Martino.
- 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_69ca8364e13081909c85fe80f44fe86f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc600d267c81909e145e58af08e523 |
completed | April 1, 2026, midnight |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6fc1579c8190ade0f780183aa1dc |
completed | April 3, 2026, 7:44 a.m. |
Created at: March 30, 2026, 6:46 p.m.