Triple
T25816421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shruti Haasan |
E650267
|
entity |
| Predicate | debutWorkAsActress |
P60224
|
FINISHED |
| Object |
Luck
Luck is a 2009 Indian action-thriller film known for its high-stakes plot centered on deadly games of chance.
|
E1698241
|
NE FINISHED |
How this triple was built (3 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: Luck | Statement: [Shruti Haasan, debutWorkAsActress, Luck]
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: Luck Triple: [Shruti Haasan, debutWorkAsActress, Luck]
Generated description
Luck is a 2009 Indian action-thriller film known for its high-stakes plot centered on deadly games of chance.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: debutWorkAsActress Context triple: [Shruti Haasan, debutWorkAsActress, Luck]
-
A.
debutAsLeadActressYear
Indicates the year in which an entity first made her debut as a lead actress.
-
B.
debutInCinema
Indicates that a film or work is first publicly released or shown in movie theaters.
-
C.
startedActingCareer
Indicates that an entity began their professional work or involvement in acting at a specific time or event.
-
D.
madeProfessionalDebutIn
chosen
Indicates the time or event in which an individual first performed or appeared in a professional capacity within a given field or organization.
-
E.
startedCareerAsChildActor
Indicates that a person began their professional career in acting during childhood.
- F. None of above.
Provenance (6 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_69e7ab367fcc8190a5ff1e7f3da046a4 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f600c915908190aea4b20f6b9b11b6 |
completed | May 2, 2026, 1:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a10da1fb11c8190b04a22f08ae54848 |
completed | May 22, 2026, 10:35 p.m. |
| NEDg | Description generation | batch_6a10dd1a43e48190992000544b46724b |
completed | May 22, 2026, 10:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a10df81838881908011d553f260e7a0 |
completed | May 22, 2026, 10:58 p.m. |
| PD | Predicate disambiguation | batch_69f4a0fed15881909b789251fe5d8d45 |
completed | May 1, 2026, 12:47 p.m. |
Created at: April 22, 2026, 7:26 a.m.