Triple
T5642015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Faris |
E124287
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Ben Indra
Ben Indra is an American actor known for his supporting roles in film and television and for his former marriage to actress Anna Faris.
|
E535256
|
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: Ben Indra | Statement: [Anna Faris, spouse, Ben Indra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ben Indra Context triple: [Anna Faris, spouse, Ben Indra]
-
A.
Yadnya Kasada
Yadnya Kasada is a traditional Tenggerese Hindu ritual in which offerings are cast into the crater of Mount Bromo in East Java, Indonesia, to honor ancestral spirits and deities.
-
B.
Gordhan
Gordhan is the surname of Pravin Gordhan, a prominent South African politician and former finance minister.
-
C.
Mahendra
Mahendra is a central fictional character in Rabindranath Tagore’s Bengali novel "Chokher Bali," whose complex relationships drive much of the story’s emotional conflict.
-
D.
Ashok Chandra
Ashok Chandra is a computer scientist known for his contributions to theoretical computer science and complexity theory.
-
E.
Sikandra Rao
Sikandra Rao is a town in the Braj cultural region of Uttar Pradesh, India, known for its agrarian economy and regional trade.
- 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: Ben Indra Triple: [Anna Faris, spouse, Ben Indra]
Generated description
Ben Indra is an American actor known for his supporting roles in film and television and for his former marriage to actress Anna Faris.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ben Indra Target entity description: Ben Indra is an American actor known for his supporting roles in film and television and for his former marriage to actress Anna Faris.
-
A.
Yadnya Kasada
Yadnya Kasada is a traditional Tenggerese Hindu ritual in which offerings are cast into the crater of Mount Bromo in East Java, Indonesia, to honor ancestral spirits and deities.
-
B.
Gordhan
Gordhan is the surname of Pravin Gordhan, a prominent South African politician and former finance minister.
-
C.
Mahendra
Mahendra is a central fictional character in Rabindranath Tagore’s Bengali novel "Chokher Bali," whose complex relationships drive much of the story’s emotional conflict.
-
D.
Ashok Chandra
Ashok Chandra is a computer scientist known for his contributions to theoretical computer science and complexity theory.
-
E.
Sikandra Rao
Sikandra Rao is a town in the Braj cultural region of Uttar Pradesh, India, known for its agrarian economy and regional trade.
- 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.