Triple
T9549416
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arsenal (1929 film) |
E230379
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Tymish |
E518447
|
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: Tymish | Statement: [Arsenal (1929 film), mainCharacter, Tymish]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tymish Context triple: [Arsenal (1929 film), mainCharacter, Tymish]
-
A.
Tymofiy
chosen
Tymofiy is a masculine given name of Ukrainian origin, notably borne by economist and former Ukrainian minister Tymofiy Mylovanov.
-
B.
Tupolski
Tupolski is a hard-edged, morally ambiguous police detective in Martin McDonagh’s dark play "The Pillowman," known for his interrogations and psychological manipulation.
-
C.
Taaffe
Taaffe is a surname of Irish origin borne by various notable individuals across fields such as politics, the arts, and academia.
-
D.
Taras
Taras is the ancient Greek city in southern Italy that later became known by its Roman name Tarentum.
-
E.
Turchynov
Turchynov is a Ukrainian politician and former acting president of Ukraine known for his roles in the country’s post-2014 political transition.
- 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_69ca847d3be8819099c9dad2a7e786f1 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99059138819088ae54b26df979cf |
completed | April 1, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d14c82c98c8190a4fd6fc3ceb4173d |
completed | April 4, 2026, 5:38 p.m. |
Created at: March 30, 2026, 8:02 p.m.