Triple
T10423963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simo Häyhä |
E245735
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Simo |
E245734
|
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: Simo | Statement: [Simo Häyhä, givenName, Simo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Simo Context triple: [Simo Häyhä, givenName, Simo]
-
A.
Simo
chosen
Simo is a Finnish given name most famously borne by Simo Häyhä, a legendary World War II sniper.
-
B.
Simoni
Simoni is an Italian surname most notably associated with Gigi Simoni, a respected former football player and manager.
-
C.
Simm
Simm is an English surname most notably associated with actor John Simm, known for his roles in British television and film.
-
D.
Sven
Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
-
E.
Simme
The Simme is a river in the Swiss canton of Bern that flows through the Bernese Oberland, known for its alpine scenery and contribution to the region’s hydropower and tourism.
- 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_69d381bf3dc08190bf35a2643e4e8f22 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea2de4d48190aee65b3f6ec3cc48 |
completed | April 7, 2026, 11:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7fc2160208190b6384190d9537df4 |
completed | April 9, 2026, 7:21 p.m. |
Created at: April 6, 2026, 12:12 p.m.