Triple
T12415643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ivan Sratsimir |
E296627
|
entity |
| Predicate | sibling |
P363
|
FINISHED |
| Object |
Keratsa-Maria
Keratsa-Maria was a 14th-century Bulgarian princess of the Second Bulgarian Empire, known as the daughter of Tsar Ivan Alexander and sister of Tsar Ivan Sratsimir.
|
E980934
|
NE FINISHED |
How this triple was built (4 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: Keratsa-Maria | Statement: [Ivan Sratsimir, sibling, Keratsa-Maria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keratsa-Maria Context triple: [Ivan Sratsimir, sibling, Keratsa-Maria]
-
A.
Kerstin
Kerstin is a feminine given name of Scandinavian origin, particularly common in Sweden and other Nordic countries.
-
B.
Katiaan
Katiaan is a rural barangay (village-level administrative division) of the municipality of Maitum in the province of Sarangani, Philippines.
-
C.
Kaarina
Kaarina is a town and municipality in southwestern Finland, located near the city of Turku.
-
D.
Marya
Marya is a feminine given name, often considered a variant of Mary and used in various cultures and languages.
-
E.
Klaudia
Klaudia is the feminine given name corresponding to the male name Klaus, commonly used in various European countries.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Keratsa-Maria Triple: [Ivan Sratsimir, sibling, Keratsa-Maria]
Generated description
Keratsa-Maria was a 14th-century Bulgarian princess of the Second Bulgarian Empire, known as the daughter of Tsar Ivan Alexander and sister of Tsar Ivan Sratsimir.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Keratsa-Maria Target entity description: Keratsa-Maria was a 14th-century Bulgarian princess of the Second Bulgarian Empire, known as the daughter of Tsar Ivan Alexander and sister of Tsar Ivan Sratsimir.
-
A.
Kerstin
Kerstin is a feminine given name of Scandinavian origin, particularly common in Sweden and other Nordic countries.
-
B.
Katiaan
Katiaan is a rural barangay (village-level administrative division) of the municipality of Maitum in the province of Sarangani, Philippines.
-
C.
Kaarina
Kaarina is a town and municipality in southwestern Finland, located near the city of Turku.
-
D.
Marya
Marya is a feminine given name, often considered a variant of Mary and used in various cultures and languages.
-
E.
Klaudia
Klaudia is the feminine given name corresponding to the male name Klaus, commonly used in various European countries.
- F. None of above. chosen
Provenance (5 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d6c4f6c8190bc99d3f7b64205c3 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f634915940819092665f1f35aa823d |
completed | May 2, 2026, 5:29 p.m. |
| NEDg | Description generation | batch_69f635997b088190b6207fcac5594eb2 |
completed | May 2, 2026, 5:34 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f636d727a08190882eec3fd664b64d |
completed | May 2, 2026, 5:39 p.m. |
Created at: April 8, 2026, 9:55 p.m.