Triple
T16610082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mehta |
E403542
|
entity |
| Predicate | hasVariantSpelling |
P457
|
FINISHED |
| Object | Mehhta |
E403542
|
NE FINISHED |
How this triple was built (2 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: Mehhta | Statement: [Mehta, hasVariantSpelling, Mehhta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mehhta Context triple: [Mehta, hasVariantSpelling, Mehhta]
-
A.
Mehta
chosen
Mehta is a common Indian surname associated with various communities, often linked to professions such as merchants, accountants, and administrators.
-
B.
Phillauri
Phillauri is a 2017 Indian romantic comedy-drama film that blends elements of fantasy and reincarnation, featuring a ghost bride entangled in a modern-day Punjabi wedding.
-
C.
Dilwaala
Dilwaala is an Indian film best known for featuring actress Persis Khambatta in a notable role.
-
D.
Sairat
Sairat is a critically acclaimed and commercially successful Marathi romantic drama film known for its powerful portrayal of caste and class conflict in rural India.
-
E.
Mardaani
Mardaani is a 2014 Indian crime thriller film that follows a tough female police officer’s pursuit of a child trafficking racket.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3609572508190a5d7e6c3e0a8cf95 |
completed | April 18, 2026, 10:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0075aca5c0819092637e0d83ce8ac0 |
completed | May 10, 2026, 12:10 p.m. |
Created at: April 10, 2026, 5:17 a.m.