Triple
T12248365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Masquerade |
E291907
|
entity |
| Predicate | featuresArtist |
P1952
|
FINISHED |
| Object |
Sharissa
Sharissa is a musical artist known for her featured performance on the track "Masquerade."
|
E971069
|
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: Sharissa | Statement: [Masquerade, featuresArtist, Sharissa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sharissa Context triple: [Masquerade, featuresArtist, Sharissa]
-
A.
Shara
Shara is an ancient Mesopotamian deity, primarily known as the warrior god and tutelary divine figure associated with the city-state of Umma in Sumer.
-
B.
Sheilia
Sheilia is a feminine given name, typically considered an alternative spelling of the name Sheila.
-
C.
Arnissa
Arnissa is a small town in northern Greece situated close to Lake Vegoritida, known for its scenic lakeside setting and surrounding natural landscape.
-
D.
Ta’aisha
The Ta’aisha are a Sudanese Arab tribal group from the Darfur–Kordofan region, historically prominent through their leadership role in the Mahdist state under Abdallahi ibn Muhammad.
-
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: Sharissa Triple: [Masquerade, featuresArtist, Sharissa]
Generated description
Sharissa is a musical artist known for her featured performance on the track "Masquerade."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sharissa Target entity description: Sharissa is a musical artist known for her featured performance on the track "Masquerade."
-
A.
Shara
Shara is an ancient Mesopotamian deity, primarily known as the warrior god and tutelary divine figure associated with the city-state of Umma in Sumer.
-
B.
Sheilia
Sheilia is a feminine given name, typically considered an alternative spelling of the name Sheila.
-
C.
Arnissa
Arnissa is a small town in northern Greece situated close to Lake Vegoritida, known for its scenic lakeside setting and surrounding natural landscape.
-
D.
Ta’aisha
The Ta’aisha are a Sudanese Arab tribal group from the Darfur–Kordofan region, historically prominent through their leadership role in the Mahdist state under Abdallahi ibn Muhammad.
-
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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cc50d808190a3c8d1ada31a6a91 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60ab9a9b08190903c1ce6d91af2b5 |
completed | May 2, 2026, 2:31 p.m. |
| NEDg | Description generation | batch_69f60bdd8d508190813178ff4c77afcf |
completed | May 2, 2026, 2:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f60c67c680819087630d190d0a008f |
completed | May 2, 2026, 2:38 p.m. |
Created at: April 8, 2026, 9:51 p.m.