Triple
T15536948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thereza |
E370371
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Tereza |
E728423
|
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: Tereza | Statement: [Thereza, relatedName, Tereza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tereza Context triple: [Thereza, relatedName, Tereza]
-
A.
Tereza
chosen
Tereza is a feminine given name, commonly used in various European countries as a variant of Theresa.
-
B.
Tereza Vávrová
Tereza Vávrová is a notable bearer of the Czech surname Vávrová, recognized enough to be specifically cited in reference to the name.
-
C.
Antónia
Antónia is a feminine given name commonly used in various European languages, often as a variant of Antonia.
-
D.
Helena Davidová
Helena Davidová is a member of Franz Kafka’s extended family, known as the daughter of his sister Ottla Kafka.
-
E.
Milena
Milena is a small town and comune in the Province of Caltanissetta in central Sicily, Italy.
- 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_69d85cc521a08190921fb50319dddc34 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e0442f3c688190a599165e526af2ed |
completed | April 16, 2026, 2:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d605b908190a18c63142c8bb854 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 4:06 a.m.