Triple
T10883369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mihaly Csikszentmihalyi |
E256978
|
entity |
| Predicate | citizenship |
P2
|
FINISHED |
| Object | Hungary |
E5017
|
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: Hungary | Statement: [Mihaly Csikszentmihalyi, citizenship, Hungary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hungary Context triple: [Mihaly Csikszentmihalyi, citizenship, Hungary]
-
A.
Hungary
chosen
Hungary is a landlocked Central European country known for its rich history, distinct language (Hungarian), and capital city Budapest, famed for its thermal baths and architecture.
-
B.
Austria and Hungary
Austria and Hungary are neighboring Central European countries with closely linked histories, cultures, and transportation networks.
-
C.
Ungar
Ungar is a surname of Germanic and Central European origin, historically associated with people from Hungary or of Hungarian descent.
-
D.
Slovakia and Hungary
Slovakia and Hungary are neighboring Central European countries that share a significant stretch of their border along the Danube River.
-
E.
Havran
Havran is a town and district in western Turkey known for its agricultural production and location within Balıkesir Province.
- 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_69d6aa848804819081b2713ca0bedf06 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d751db24208190b3a7ed7eea118522 |
completed | April 9, 2026, 7:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dff7ca05788190aff4f34084499ba6 |
completed | April 15, 2026, 8:40 p.m. |
Created at: April 8, 2026, 9:21 p.m.