Triple
T5642029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Faris |
E124287
|
entity |
| Predicate | instagramUsername |
P3718
|
FINISHED |
| Object |
annafaris
Anna Faris is an American actress, comedian, and podcaster best known for her roles in the "Scary Movie" film series and the sitcom "Mom."
|
E535258
|
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: annafaris | Statement: [Anna Faris, instagramUsername, annafaris]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: annafaris Context triple: [Anna Faris, instagramUsername, annafaris]
-
A.
Annang
Annang is a prominent ethnic group in southeastern Nigeria, known for its rich cultural heritage, language, and traditions, particularly within Akwa Ibom State.
-
B.
Afif
Afif is a town in central Saudi Arabia known as an inland community within the Riyadh administrative region.
-
C.
Æfsati
Æfsati is a hunting and wildlife deity in Ossetian folk religion, revered as the protector of hunters and animals in the mountains and forests.
-
D.
Nuffar
Nuffar is the modern name for the archaeological mound that marks the site of the ancient Sumerian city of Nippur in present-day Iraq.
-
E.
Annette
Annette is a feminine given name of French origin, commonly used in various European and English-speaking countries.
- 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: annafaris Triple: [Anna Faris, instagramUsername, annafaris]
Generated description
Anna Faris is an American actress, comedian, and podcaster best known for her roles in the "Scary Movie" film series and the sitcom "Mom."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: annafaris Target entity description: Anna Faris is an American actress, comedian, and podcaster best known for her roles in the "Scary Movie" film series and the sitcom "Mom."
-
A.
Annang
Annang is a prominent ethnic group in southeastern Nigeria, known for its rich cultural heritage, language, and traditions, particularly within Akwa Ibom State.
-
B.
Afif
Afif is a town in central Saudi Arabia known as an inland community within the Riyadh administrative region.
-
C.
Æfsati
Æfsati is a hunting and wildlife deity in Ossetian folk religion, revered as the protector of hunters and animals in the mountains and forests.
-
D.
Nuffar
Nuffar is the modern name for the archaeological mound that marks the site of the ancient Sumerian city of Nippur in present-day Iraq.
-
E.
Annette
Annette is a feminine given name of French origin, commonly used in various European and English-speaking countries.
- 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_69c00824643c81909ffdb888a2d35189 |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022a6a22881908d16f4df564ed2a2 |
completed | March 22, 2026, 5:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04d7c98008190b79528596eca4208 |
completed | March 22, 2026, 8:13 p.m. |
| NEDg | Description generation | batch_69c04edaa7408190811007d27549a35d |
completed | March 22, 2026, 8:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c04ff40ce88190a9aa8886c22386e1 |
completed | March 22, 2026, 8:24 p.m. |
Created at: March 22, 2026, 3:41 p.m.