Triple
T10017650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonis |
E199534
|
entity |
| Predicate | shortFormOf |
P43
|
FINISHED |
| Object | Antonios |
E199534
|
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: Antonios | Statement: [Antonis, shortFormOf, Antonios]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Antonios Context triple: [Antonis, shortFormOf, Antonios]
-
A.
Antonis
chosen
Antonis is a Greek given name, commonly used as a variant of Anthony.
-
B.
Antonino
Antonino is the Italian given name of actor Giovanni Ribisi, whose full name is Antonino Giovanni Ribisi.
-
C.
Eugenios
Eugenios is a variant form of the given name Eugenio, commonly used in Greek and related linguistic traditions.
-
D.
Aristo
Aristo is an AI research project by the Allen Institute for Artificial Intelligence focused on solving standardized science and math exams using natural language understanding and reasoning.
-
E.
Stephanos
Stephanos is the Greek form of the name Stephen, traditionally associated with Saint Stephen, the first Christian martyr.
- 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_69ca8315a1a08190ab310f25620f362b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd4de1588190a89ed575cff0b8c9 |
completed | April 2, 2026, 1:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2821b22488190913d743bc40a4c8e |
completed | April 5, 2026, 3:39 p.m. |
Created at: March 30, 2026, 8:53 p.m.