Triple
T9616178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Honey, I Shrunk the Audience |
E232224
|
entity |
| Predicate | featuresActor |
P15562
|
FINISHED |
| Object | Joshua Shalikar |
E810516
|
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: Joshua Shalikar | Statement: [Honey, I Shrunk the Audience, featuresActor, Joshua Shalikar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joshua Shalikar Context triple: [Honey, I Shrunk the Audience, featuresActor, Joshua Shalikar]
-
A.
Ashwanth Ashokkumar
Ashwanth Ashokkumar is an Indian child actor best known for his acclaimed performance in the Tamil film "Super Deluxe."
-
B.
Daniel Shalikar
chosen
Daniel Shalikar is an actor best known for his role in the 3D Disney theme park attraction film "Honey, I Shrunk the Audience."
-
C.
Jay Mehta
Jay Mehta is an Indian businessman and industrialist, known for his interests in cement and other industries and for being married to actress Juhi Chawla.
-
D.
Chemban Vinod Jose
Chemban Vinod Jose is an Indian actor, screenwriter, and producer primarily known for his character roles in Malayalam cinema.
-
E.
Yogesh Chandrahasan
Yogesh Chandrahasan is an Indian architect best known for designing the National War Memorial in New Delhi.
- 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_69ca84867bb88190b4b57dd5a56d5691 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9aad71a0819084ea00c2409e9922 |
completed | April 1, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18225f9508190bde23b9d2a40bccc |
completed | April 4, 2026, 9:27 p.m. |
Created at: March 30, 2026, 8:09 p.m.