Triple
T14634566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alonzo "Fonny" Hunt |
E343569
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Alonzo |
E207978
|
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: Alonzo | Statement: [Alonzo "Fonny" Hunt, givenName, Alonzo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alonzo Context triple: [Alonzo "Fonny" Hunt, givenName, Alonzo]
-
A.
Alonzo
chosen
Alonzo is a given name most notably borne by the American mathematician and logician Alonzo Church, a founder of theoretical computer science and lambda calculus.
-
B.
Alonzo
Alonzo is a minor character in Disney’s live-action film "102 Dalmatians," appearing as one of the dogs involved in the story’s adventure.
-
C.
Alonzo Miller
Alonzo Miller is a songwriter best known for co-writing the hit track "Super Freaky Girl."
-
D.
John Alonzo
John Alonzo was an influential American cinematographer best known for his innovative, atmospheric work on films such as "Chinatown" and "Scarface."
-
E.
Antwan
Antwan is the egotistical, money-obsessed CEO and game publisher who serves as the main antagonist in the action-comedy film "Free Guy."
- 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_69d822dffc3c8190aa173b90761bffda |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4ab9578819085b4cf7244d30d87 |
completed | April 14, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda933937881909f3cf59fba878dfd |
completed | May 8, 2026, 9:13 a.m. |
Created at: April 10, 2026, 1:26 a.m.