Triple
T21865945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acqua Vergine |
E539881
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Rome |
—
|
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: Rome | Statement: [Acqua Vergine, cityServed, Rome]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rome Context triple: [Acqua Vergine, cityServed, Rome]
-
A.
Rome
Rome is a charismatic and confident male stripper and emcee who runs an upscale exotic entertainment venue in the film "Magic Mike XXL."
-
B.
Rome
chosen
Rome is the historic capital of Italy and a major cultural and religious center of the world, renowned for its ancient Roman heritage, art, and architecture.
-
C.
Rome
Rome is a British-American animated television series created by Danger Mouse that blends surreal humor, espionage themes, and distinctive visual style.
-
D.
Rome
Rome is the codename for a generation of AMD EPYC server processors based on the Zen 2 microarchitecture, known for significant improvements in performance and efficiency over its predecessors.
-
E.
Rome
Rome is a city in northwestern Georgia, United States, known as a regional center for education, healthcare, and manufacturing in the Appalachian foothills.
- 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_69e0c478f59081909d54302b57fc1ce3 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0d63f2ec48190956a3e99d8f98b1f |
completed | April 28, 2026, 3:46 p.m. |
Created at: April 16, 2026, 6:56 p.m.