Triple
T3472000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Théodore |
E73281
|
entity |
| Predicate | hasCognate |
P2525
|
FINISHED |
| Object | Todor |
E270314
|
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: Todor | Statement: [Théodore, hasCognate, Todor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Todor Context triple: [Théodore, hasCognate, Todor]
-
A.
Alexander Toshev
Alexander Toshev is a computer scientist known for his contributions to computer vision and deep learning, including influential work on object detection.
-
B.
Theodore Svetoslav
Theodore Svetoslav was a medieval Bulgarian tsar who restored and strengthened the Second Bulgarian Empire in the early 14th century through military successes and internal consolidation.
-
C.
Mario Grigorov
Mario Grigorov is a Bulgarian-born composer and pianist best known for his film scores and collaborations with director Lee Daniels.
-
D.
Teodor
chosen
Teodor is a given name, commonly used in various European languages, that corresponds to the English name Theodore.
-
E.
Petar
Petar is a given name commonly used in Slavic countries, equivalent to the English name Peter.
- 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_69ad85b2fed48190948c8765e453d270 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb3cc8488190b97c732e3f600a90 |
completed | March 8, 2026, 6:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b36810958c81908982e0ef996dc480 |
completed | March 13, 2026, 1:27 a.m. |
Created at: March 8, 2026, 3:17 p.m.