Triple
T22959728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Román |
E570859
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Román |
—
|
NE NERFINISHED |
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: Román | Statement: [San Román, hasComponent, Román]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Román Context triple: [San Román, hasComponent, Román]
-
A.
Román
chosen
Román is a given name and surname of Latin origin, commonly used in Spanish-speaking countries.
-
B.
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.
-
C.
Romano
Romano is an Italian masculine given name derived from the Latin word for "Roman" or "from Rome."
-
D.
Romão
Romão is a Portuguese given name and surname derived from the Latin name Romanus, commonly associated with Roman heritage.
-
E.
Romana
Romana is a highly intelligent and compassionate Time Lady from the Doctor Who universe who serves as one of the Doctor’s most capable and scholarly companions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b212a88190b5259caf51606084 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f181f3c96081909abd6ec32103d4c3 |
completed | April 29, 2026, 3:58 a.m. |
Created at: April 17, 2026, 3:47 p.m.