Triple
T13221690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Servius |
E314768
|
entity |
| Predicate | hasGenderForm |
P6042
|
FINISHED |
| Object |
Servia (feminine, rare)
Servia is a rare feminine given name derived from the ancient Roman name Servius.
|
E1027867
|
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: Servia (feminine, rare) | Statement: [Servius, hasGenderForm, Servia (feminine, rare)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Servia (feminine, rare) Context triple: [Servius, hasGenderForm, Servia (feminine, rare)]
-
A.
Anna Neda of Serbia
Anna Neda of Serbia was a medieval Serbian princess and Bulgarian tsarina who became queen consort through her marriage to Tsar Michael Shishman of Bulgaria.
-
B.
Marić
Marić is the Serbian family name of Mileva Marić, a pioneering physicist and mathematician known for her association with Albert Einstein.
-
C.
Milana Savić
Milana Savić is the mother of Serbian professional footballer Sergej Milinković-Savić.
-
D.
Olivera Despina
Olivera Despina was a Serbian princess of the Lazarević dynasty who became an Ottoman sultana through her marriage to Sultan Bayezid I.
-
E.
Mara Lazarević
Mara Lazarević was a medieval Serbian noblewoman, known as a daughter of Prince Lazar of Serbia and a member of the influential Lazarević dynasty.
- 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: Servia (feminine, rare) Triple: [Servius, hasGenderForm, Servia (feminine, rare)]
Generated description
Servia is a rare feminine given name derived from the ancient Roman name Servius.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Servia (feminine, rare) Target entity description: Servia is a rare feminine given name derived from the ancient Roman name Servius.
-
A.
Anna Neda of Serbia
Anna Neda of Serbia was a medieval Serbian princess and Bulgarian tsarina who became queen consort through her marriage to Tsar Michael Shishman of Bulgaria.
-
B.
Marić
Marić is the Serbian family name of Mileva Marić, a pioneering physicist and mathematician known for her association with Albert Einstein.
-
C.
Milana Savić
Milana Savić is the mother of Serbian professional footballer Sergej Milinković-Savić.
-
D.
Olivera Despina
Olivera Despina was a Serbian princess of the Lazarević dynasty who became an Ottoman sultana through her marriage to Sultan Bayezid I.
-
E.
Mara Lazarević
Mara Lazarević was a medieval Serbian noblewoman, known as a daughter of Prince Lazar of Serbia and a member of the influential Lazarević dynasty.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98cf581508190883033f0c961736a |
completed | April 10, 2026, 11:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6ff2282fc8190bc5037ff62e594ff |
completed | May 3, 2026, 7:54 a.m. |
| NEDg | Description generation | batch_69f7001924d48190af7d430cb258409a |
completed | May 3, 2026, 7:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f700cfb43881909c903ec12b15065a |
completed | May 3, 2026, 8:01 a.m. |
Created at: April 9, 2026, 9:18 p.m.