Triple
T13672342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ali Kiba |
E327780
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Prince Sameer
Prince Sameer is one of the children of Tanzanian singer and songwriter Ali Kiba.
|
E1053043
|
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: Prince Sameer | Statement: [Ali Kiba, hasChild, Prince Sameer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Prince Sameer Context triple: [Ali Kiba, hasChild, Prince Sameer]
-
A.
Prince Naveen
Prince Naveen is the charming, carefree prince from Disney’s "The Princess and the Frog," known for his transformation into a frog and eventual romance with Tiana.
-
B.
Prince Mahesh
Prince Mahesh is the popular nickname of Mahesh Babu, a leading Telugu film actor and producer known for his work in South Indian cinema.
-
C.
Prince Naseem
Prince Naseem is the ring name of Naseem Hamed, a flamboyant British former professional boxer renowned for his explosive knockout power and unorthodox style in the featherweight division.
-
D.
Ali Khan
Ali Khan was a prominent khan of the Siberian Khanate, remembered as one of its most significant and influential rulers.
-
E.
Shamsher
Shamsher is the given first name of legendary Indian film actor and director Shammi Kapoor, a major star of Hindi cinema’s golden era.
- 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: Prince Sameer Triple: [Ali Kiba, hasChild, Prince Sameer]
Generated description
Prince Sameer is one of the children of Tanzanian singer and songwriter Ali Kiba.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Prince Sameer Target entity description: Prince Sameer is one of the children of Tanzanian singer and songwriter Ali Kiba.
-
A.
Prince Naveen
Prince Naveen is the charming, carefree prince from Disney’s "The Princess and the Frog," known for his transformation into a frog and eventual romance with Tiana.
-
B.
Prince Mahesh
Prince Mahesh is the popular nickname of Mahesh Babu, a leading Telugu film actor and producer known for his work in South Indian cinema.
-
C.
Prince Naseem
Prince Naseem is the ring name of Naseem Hamed, a flamboyant British former professional boxer renowned for his explosive knockout power and unorthodox style in the featherweight division.
-
D.
Ali Khan
Ali Khan was a prominent khan of the Siberian Khanate, remembered as one of its most significant and influential rulers.
-
E.
Shamsher
Shamsher is the given first name of legendary Indian film actor and director Shammi Kapoor, a major star of Hindi cinema’s golden era.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc65aab348190a6611f5765f8392d |
completed | April 12, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f78b1222648190a70f50e6e5c34593 |
completed | May 3, 2026, 5:51 p.m. |
| NEDg | Description generation | batch_69f78c0030e481909c20f21ddaa480dc |
completed | May 3, 2026, 5:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f78d6d74bc8190ad5476a06e8fd8ad |
completed | May 3, 2026, 6:01 p.m. |
Created at: April 9, 2026, 9:53 p.m.