Triple
T10989352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nasib Yusifbeyli |
E259714
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Nasib
Nasib is a given name most notably borne by Nasib Yusifbeyli, an Azerbaijani statesman and political figure of the early 20th century.
|
E898411
|
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: Nasib | Statement: [Nasib Yusifbeyli, givenName, Nasib]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nasib Context triple: [Nasib Yusifbeyli, givenName, Nasib]
-
A.
Kusaila
Kusaila was a 7th-century Berber Christian leader and military commander who led resistance against the early Muslim expansion in North Africa.
-
B.
Nagode
"Nagode" is a popular Afro-pop song by Nigerian singer Yemi Alade, known for its catchy melody and blend of contemporary and African musical elements.
-
C.
Nzaman
Nzaman is a dialect of the Fang language spoken by Fang communities in Central Africa.
-
D.
Toinette
Toinette is the sharp-witted, outspoken maid in Molière’s comedy "Le Malade imaginaire," known for her clever schemes and satirical commentary on her hypochondriac master.
-
E.
Nahan
Nahan is a small hill town and municipal council in Himachal Pradesh, India, known for its scenic surroundings, pleasant climate, and role as a regional administrative and commercial center.
- 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: Nasib Triple: [Nasib Yusifbeyli, givenName, Nasib]
Generated description
Nasib is a given name most notably borne by Nasib Yusifbeyli, an Azerbaijani statesman and political figure of the early 20th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nasib Target entity description: Nasib is a given name most notably borne by Nasib Yusifbeyli, an Azerbaijani statesman and political figure of the early 20th century.
-
A.
Kusaila
Kusaila was a 7th-century Berber Christian leader and military commander who led resistance against the early Muslim expansion in North Africa.
-
B.
Nagode
"Nagode" is a popular Afro-pop song by Nigerian singer Yemi Alade, known for its catchy melody and blend of contemporary and African musical elements.
-
C.
Nzaman
Nzaman is a dialect of the Fang language spoken by Fang communities in Central Africa.
-
D.
Toinette
Toinette is the sharp-witted, outspoken maid in Molière’s comedy "Le Malade imaginaire," known for her clever schemes and satirical commentary on her hypochondriac master.
-
E.
Nahan
Nahan is a small hill town and municipal council in Himachal Pradesh, India, known for its scenic surroundings, pleasant climate, and role as a regional administrative and commercial center.
- 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_69d6aa8a6a548190a750f944ccdc8064 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d787b6d0b48190aaf959e2609d34e5 |
completed | April 9, 2026, 11:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e344f95ab88190bbce8f0eab0b2713 |
completed | April 18, 2026, 8:46 a.m. |
| NEDg | Description generation | batch_69e3556e8b408190a02a1fe194ae5750 |
completed | April 18, 2026, 9:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3593b0f8481909ed7a90f8bb9839d |
completed | April 18, 2026, 10:13 a.m. |
Created at: April 8, 2026, 9:24 p.m.