Triple
T16235905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rani Mukerji |
E394109
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object |
Tanuja
Tanuja is a veteran Indian film actress known for her work in Hindi and Bengali cinema and as a prominent member of the Mukherjee-Samarth film family.
|
E1202125
|
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: Tanuja | Statement: [Rani Mukerji, relative, Tanuja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanuja Context triple: [Rani Mukerji, relative, Tanuja]
-
A.
Sujata
Sujata is a classic 1959 Hindi social drama film directed by Bimal Roy, renowned for its sensitive portrayal of caste discrimination and human relationships.
-
B.
Aruna
Aruna is a feminine given name most notably borne by Indian independence activist and political leader Aruna Asaf Ali.
-
C.
Aruna
Aruna is a figure in Hindu mythology known as the personified dawn and the divine charioteer who drives the sun god Surya across the sky.
-
D.
Madhavi
Madhavi is a celebrated courtesan and pivotal literary figure in ancient Tamil epic tradition, prominently featured in the Sangam-era works Silappatikaram and its sequel Manimekalai.
-
E.
Sunaina
Sunaina is a female given name commonly used in India and other South Asian communities.
- 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: Tanuja Triple: [Rani Mukerji, relative, Tanuja]
Generated description
Tanuja is a veteran Indian film actress known for her work in Hindi and Bengali cinema and as a prominent member of the Mukherjee-Samarth film family.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tanuja Target entity description: Tanuja is a veteran Indian film actress known for her work in Hindi and Bengali cinema and as a prominent member of the Mukherjee-Samarth film family.
-
A.
Sujata
Sujata is a classic 1959 Hindi social drama film directed by Bimal Roy, renowned for its sensitive portrayal of caste discrimination and human relationships.
-
B.
Aruna
Aruna is a feminine given name most notably borne by Indian independence activist and political leader Aruna Asaf Ali.
-
C.
Aruna
Aruna is a figure in Hindu mythology known as the personified dawn and the divine charioteer who drives the sun god Surya across the sky.
-
D.
Madhavi
Madhavi is a celebrated courtesan and pivotal literary figure in ancient Tamil epic tradition, prominently featured in the Sangam-era works Silappatikaram and its sequel Manimekalai.
-
E.
Sunaina
Sunaina is a female given name commonly used in India and other South Asian communities.
- 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_69d87f204df88190a8f88923decf9835 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2455abc608190ba3308c15c9e8a23 |
completed | April 17, 2026, 2:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a000ed8cbe48190be68ccade55211ad |
completed | May 10, 2026, 4:51 a.m. |
| NEDg | Description generation | batch_6a0010a14c488190b4a4a45b712e1e71 |
completed | May 10, 2026, 4:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0011145e2081909b0486e29e6d3e02 |
completed | May 10, 2026, 5:01 a.m. |
Created at: April 10, 2026, 5:04 a.m.