Triple
T26536410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaun Banega Crorepati |
E671262
|
entity |
| Predicate | hasLifeline |
P177510
|
FINISHED |
| Object | 50:50 |
—
|
LITERAL 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: 50:50 | Statement: [Kaun Banega Crorepati, hasLifeline, 50:50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLifeline Context triple: [Kaun Banega Crorepati, hasLifeline, 50:50]
-
A.
usesLifelines
Indicates that one entity relies on or employs lifelines (such as aids, supports, or emergency options) in the context of an activity or process.
-
B.
hasLifelinesOrAssists
Indicates that one entity provides lifelines, help, or supportive interventions to another entity.
-
C.
hasLifesavers
Indicates that one entity possesses or is associated with lifesavers (such as life-preserving devices or aids).
-
D.
hasReach
Indicates that one entity is able to extend its influence, access, or physical span to another entity or area.
-
E.
hasLie
Indicates that an entity is associated with or responsible for a specific lie or false statement.
- F. None of above. chosen
Provenance (4 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_69eeb3206e748190b90c85cc81f38c91 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f7009d39508190af7301f824615e88 |
completed | May 3, 2026, 8 a.m. |
| PD | Predicate disambiguation | batch_69f6fc53f4f881908dcc698687bbb64d |
completed | May 3, 2026, 7:42 a.m. |
| PDg | Predicate description generation | batch_69f6ffb7554881908993d6d2ffbcf8f5 |
completed | May 3, 2026, 7:56 a.m. |
Created at: April 27, 2026, 1:38 a.m.