Triple
T11697027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massimo Cellino |
E278022
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Massimo |
E739171
|
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: Massimo | Statement: [Massimo Cellino, givenName, Massimo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Massimo Context triple: [Massimo Cellino, givenName, Massimo]
-
A.
Massimo
chosen
Massimo is an Italian given name commonly used for men, equivalent to "Maximus" or "the greatest" in Latin.
-
B.
Piermarini
Piermarini is an Italian surname most notably associated with Giuseppe Piermarini, an 18th-century architect renowned for designing Milan’s Teatro alla Scala.
-
C.
Massimo Marcovaldo
Massimo Marcovaldo is a gruff but kindhearted Italian fisherman and sea monster hunter who serves as a father figure in Pixar's animated film "Luca."
-
D.
Gianni
Gianni is an Italian given name commonly used for men, often as a diminutive of Giovanni.
-
E.
Leo Cattozzo
Leo Cattozzo was an Italian film editor and inventor best known for creating the CIR-Cattozzo splicing machine, widely used in film editing.
- 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_69d6aafe02d881909900d54ad7d4af84 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a47cef60819088b7cc3a3a711e4c |
completed | April 10, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef1471cba88190a7abdcbf4f579ea9 |
completed | April 27, 2026, 7:46 a.m. |
Created at: April 8, 2026, 9:40 p.m.