Triple
T12030819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Riza Aziz |
E286399
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Riza
Riza is a masculine given name commonly used in various cultures, often with roots in Arabic and Turkish languages.
|
E961768
|
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: Riza | Statement: [Riza Aziz, givenName, Riza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Riza Context triple: [Riza Aziz, givenName, Riza]
-
A.
Rina
Rina is a feminine given name commonly used as a short or diminutive form of longer names such as Caterina.
-
B.
Zaira
Zaira is an 1829 opera in two acts by Italian composer Vincenzo Bellini, known for its bel canto style and dramatic vocal writing.
-
C.
Zenia
Zenia is a central, enigmatic and manipulative figure in Margaret Atwood's novel "The Robber Bride," whose disruptive influence profoundly affects the lives of three other women.
-
D.
Geisa
Geisa is a small historic town in the state of Thuringia in central Germany, near the former inner-German border.
-
E.
Edrisa Tanaka
Edrisa Tanaka is a quirky, enthusiastic medical examiner on the TV series "Prodigal Son," known for her darkly comic fascination with death and crime scenes.
- 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: Riza Triple: [Riza Aziz, givenName, Riza]
Generated description
Riza is a masculine given name commonly used in various cultures, often with roots in Arabic and Turkish languages.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Riza Target entity description: Riza is a masculine given name commonly used in various cultures, often with roots in Arabic and Turkish languages.
-
A.
Rina
Rina is a feminine given name commonly used as a short or diminutive form of longer names such as Caterina.
-
B.
Zaira
Zaira is an 1829 opera in two acts by Italian composer Vincenzo Bellini, known for its bel canto style and dramatic vocal writing.
-
C.
Zenia
Zenia is a central, enigmatic and manipulative figure in Margaret Atwood's novel "The Robber Bride," whose disruptive influence profoundly affects the lives of three other women.
-
D.
Geisa
Geisa is a small historic town in the state of Thuringia in central Germany, near the former inner-German border.
-
E.
Edrisa Tanaka
Edrisa Tanaka is a quirky, enthusiastic medical examiner on the TV series "Prodigal Son," known for her darkly comic fascination with death and crime scenes.
- 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_69d6ab4669e48190b59246358b0383ab |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903f24490819092ec911d6ed8e24b |
completed | April 10, 2026, 2:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49d5fd0708190860201a4a8c6fe7c |
completed | May 1, 2026, 12:32 p.m. |
| NEDg | Description generation | batch_69f53d930714819080f92d223d930389 |
completed | May 1, 2026, 11:56 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f564d2b4348190abf2d09ae00aea37 |
completed | May 2, 2026, 2:43 a.m. |
Created at: April 8, 2026, 9:47 p.m.