Triple
T22982000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hassan wa Naima |
E571492
|
entity |
| Predicate | hasMainCharacter |
P1183
|
FINISHED |
| Object |
Naima
Naima is a central romantic heroine in the classic Egyptian love story "Hassan wa Naima," often likened to a local version of "Romeo and Juliet."
|
E1565780
|
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: Naima | Statement: [Hassan wa Naima, hasMainCharacter, Naima]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Naima Context triple: [Hassan wa Naima, hasMainCharacter, Naima]
-
A.
Naima
Naima is a character in the gospel musical and film "Black Nativity," which reimagines the Nativity story through an African-American cultural and spiritual lens.
-
B.
Naima
"Naima" is a lyrical, modal jazz ballad composed by saxophonist John Coltrane, renowned for its haunting melody and emotional depth.
-
C.
Naoma
Naoma is an unincorporated community and coal-mining town located in Raleigh County, West Virginia, United States.
-
D.
Naomie
Naomie is a feminine given name most notably borne by British actress Naomie Harris.
-
E.
Katisha
Katisha is a formidable, older noblewoman and comic villainess in Gilbert and Sullivan’s operetta "The Mikado," known for her dramatic presence and unrequited love for Nanki-Poo.
- 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: Naima Triple: [Hassan wa Naima, hasMainCharacter, Naima]
Generated description
Naima is a central romantic heroine in the classic Egyptian love story "Hassan wa Naima," often likened to a local version of "Romeo and Juliet."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Naima Target entity description: Naima is a central romantic heroine in the classic Egyptian love story "Hassan wa Naima," often likened to a local version of "Romeo and Juliet."
-
A.
Naima
Naima is a character in the gospel musical and film "Black Nativity," which reimagines the Nativity story through an African-American cultural and spiritual lens.
-
B.
Naima
"Naima" is a lyrical, modal jazz ballad composed by saxophonist John Coltrane, renowned for its haunting melody and emotional depth.
-
C.
Naoma
Naoma is an unincorporated community and coal-mining town located in Raleigh County, West Virginia, United States.
-
D.
Naomie
Naomie is a feminine given name most notably borne by British actress Naomie Harris.
-
E.
Katisha
Katisha is a formidable, older noblewoman and comic villainess in Gilbert and Sullivan’s operetta "The Mikado," known for her dramatic presence and unrequited love for Nanki-Poo.
- 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_69e245b3c50481908bb3741ec9f40862 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1829645f88190aea1b96ea595ff60 |
completed | April 29, 2026, 4:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bd3730f9c8190afd3c61b7915025c |
completed | May 19, 2026, 3:05 a.m. |
| NEDg | Description generation | batch_6a0bd9ca66708190aebc81802de3f989 |
completed | May 19, 2026, 3:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bda93cce481908b4dad0b481a3b58 |
completed | May 19, 2026, 3:35 a.m. |
Created at: April 17, 2026, 3:49 p.m.