Triple
T13621338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthew Romano |
E325461
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Romano |
E942629
|
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: Romano | Statement: [Matthew Romano, familyName, Romano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Romano Context triple: [Matthew Romano, familyName, Romano]
-
A.
Romano
Romano Mussolini was an Italian jazz pianist and painter, known both for his musical career and for being the son of dictator Benito Mussolini.
-
B.
Romano
chosen
Romano is an Italian masculine given name derived from the Latin word for "Roman" or "from Rome."
-
C.
Román
Román is a given name and surname of Latin origin, commonly used in Spanish-speaking countries.
-
D.
Romitan
Romitan is a town in Uzbekistan that serves as one of the local urban centers within the Bukhara Region.
-
E.
Römer
The Römer is Frankfurt am Main’s historic city hall complex, renowned for its distinctive stepped gabled façade and long-standing role as a center of municipal government.
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0b0c9008190836242da2d6a8cbe |
completed | April 12, 2026, 2:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77fa291f48190a0ee7a228ea303bc |
completed | May 3, 2026, 5:02 p.m. |
Created at: April 9, 2026, 9:50 p.m.