Triple
T16589275
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Satish Shah |
E403038
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Madhu Shah
Madhu Shah is known as the wife of Indian film and television actor Satish Shah.
|
E1232871
|
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: Madhu Shah | Statement: [Satish Shah, spouse, Madhu Shah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Madhu Shah Context triple: [Satish Shah, spouse, Madhu Shah]
-
A.
Mani Sharma
Mani Sharma is an Indian film music composer and arranger best known for his prolific work in Telugu cinema, where he has created numerous hit soundtracks and background scores.
-
B.
Jigar Shah
Jigar Shah is a clean energy entrepreneur and investor best known as the founder of SunEdison and a prominent advocate for market-based climate solutions.
-
C.
Sujan Raskhan
Sujan Raskhan is a notable literary work associated with the poet Raskhan, reflecting themes of devotion and love in the Bhakti tradition.
-
D.
Sumedha Kailash
Sumedha Kailash is an Indian child rights activist known for her work alongside her husband, Nobel laureate Kailash Satyarthi, in rescuing and rehabilitating bonded and exploited children.
-
E.
Jyoti Devlalikar
Jyoti Devlalikar is a character in the Indian television series "Kanyadaan."
- 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: Madhu Shah Triple: [Satish Shah, spouse, Madhu Shah]
Generated description
Madhu Shah is known as the wife of Indian film and television actor Satish Shah.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Madhu Shah Target entity description: Madhu Shah is known as the wife of Indian film and television actor Satish Shah.
-
A.
Mani Sharma
Mani Sharma is an Indian film music composer and arranger best known for his prolific work in Telugu cinema, where he has created numerous hit soundtracks and background scores.
-
B.
Jigar Shah
Jigar Shah is a clean energy entrepreneur and investor best known as the founder of SunEdison and a prominent advocate for market-based climate solutions.
-
C.
Sujan Raskhan
Sujan Raskhan is a notable literary work associated with the poet Raskhan, reflecting themes of devotion and love in the Bhakti tradition.
-
D.
Sumedha Kailash
Sumedha Kailash is an Indian child rights activist known for her work alongside her husband, Nobel laureate Kailash Satyarthi, in rescuing and rehabilitating bonded and exploited children.
-
E.
Jyoti Devlalikar
Jyoti Devlalikar is a character in the Indian television series "Kanyadaan."
- 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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3599f3d18819082b3e6eef5506731 |
completed | April 18, 2026, 10:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00aae920588190b2a6b03ab1100346 |
completed | May 10, 2026, 3:57 p.m. |
| NEDg | Description generation | batch_6a00ab6a378c81909617da9720b51161 |
completed | May 10, 2026, 3:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00abf09fe4819082ee0c6c6f702822 |
completed | May 10, 2026, 4:01 p.m. |
Created at: April 10, 2026, 5:16 a.m.