Triple
T16108163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goran Visnjic |
E390796
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Goran |
E859330
|
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: Goran | Statement: [Goran Visnjic, givenName, Goran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Goran Context triple: [Goran Visnjic, givenName, Goran]
-
A.
Goran
chosen
Goran is a masculine given name commonly used in various Slavic countries.
-
B.
Borjan
Borjan is a surname most notably borne by Milan Borjan, a Canadian professional soccer goalkeeper.
-
C.
Vlatko
Vlatko is a masculine given name commonly used in Slavic countries, particularly in North Macedonia and other parts of the Balkans.
-
D.
Danijel
Danijel is the central male protagonist in the war drama film "In the Land of Blood and Honey," which explores a complex relationship set against the backdrop of the Bosnian War.
-
E.
Branko
Branko is a masculine given name commonly used in Slavic countries, particularly in the Balkans.
- 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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e20165aa9c81908c5358cca2b0d0fe |
completed | April 17, 2026, 9:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeba4479c81909f7d43e33f228f7e |
completed | May 10, 2026, 2:21 a.m. |
Created at: April 10, 2026, 5 a.m.