Triple
T20602442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hatice Sultan |
E506216
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Hatice
Hatice is a feminine given name of Turkish and Arabic origin, commonly borne by Ottoman princesses and women in Muslim-majority cultures.
|
E669881
|
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: Hatice | Statement: [Hatice Sultan, givenName, Hatice]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hatice Context triple: [Hatice Sultan, givenName, Hatice]
-
A.
Hülya
Hülya is a feminine given name of Turkish origin commonly used in Turkey and among Turkish communities.
-
B.
Nergiz
Nergiz is a neighborhood within the Karşıyaka district of İzmir, Turkey, known as a residential and commercial area on the city’s northern shore.
-
C.
Hatice Hatun
Hatice Hatun was an Ottoman consort known primarily as one of the wives of Sultan Murad II in the 15th century.
-
D.
Gülbahar
Gülbahar is the tragic heroine of the Turkish novel "Ağrıdağı Efsanesi," whose love story unfolds against the backdrop of Mount Ararat’s legendary landscape.
-
E.
Türkan
Türkan is a biographical novel by Turkish author Ayşe Kulin that portrays the life and struggles of pioneering Turkish doctor and women’s rights advocate Türkan Saylan.
- 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: Hatice Triple: [Hatice Sultan, givenName, Hatice]
Generated description
Hatice is a feminine given name of Turkish and Arabic origin, commonly borne by Ottoman princesses and women in Muslim-majority cultures.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hatice Target entity description: Hatice is a feminine given name of Turkish and Arabic origin, commonly borne by Ottoman princesses and women in Muslim-majority cultures.
-
A.
Hülya
Hülya is a feminine given name of Turkish origin commonly used in Turkey and among Turkish communities.
-
B.
Nergiz
Nergiz is a neighborhood within the Karşıyaka district of İzmir, Turkey, known as a residential and commercial area on the city’s northern shore.
-
C.
Hatice Hatun
chosen
Hatice Hatun was an Ottoman consort known primarily as one of the wives of Sultan Murad II in the 15th century.
-
D.
Gülbahar
Gülbahar is the tragic heroine of the Turkish novel "Ağrıdağı Efsanesi," whose love story unfolds against the backdrop of Mount Ararat’s legendary landscape.
-
E.
Türkan
Türkan is a biographical novel by Turkish author Ayşe Kulin that portrays the life and struggles of pioneering Turkish doctor and women’s rights advocate Türkan Saylan.
- F. None of above.
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_69e0b4ba6ae88190af871e1f9522c704 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aa20f5c881909265ce7d96efc487 |
completed | April 20, 2026, 10:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08b3f35cb08190a7cdc354534fba1c |
completed | May 16, 2026, 6:14 p.m. |
| NEDg | Description generation | batch_6a08b4c5946081909ac59ed47f3ba909 |
completed | May 16, 2026, 6:17 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08b53450a481909133bd18c34dc78c |
completed | May 16, 2026, 6:19 p.m. |
Created at: April 16, 2026, 11:41 a.m.