Triple
T7122003
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jatra |
E165969
|
entity |
| Predicate | facedChange |
P74970
|
FINISHED |
| Object | competition from cinema and television |
—
|
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: competition from cinema and television | Statement: [Jatra, facedChange, competition from cinema and television]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: facedChange Context triple: [Jatra, facedChange, competition from cinema and television]
-
A.
facedEvent
Indicates that an entity directly encountered, experienced, or dealt with a particular event.
-
B.
facedBy
Indicates that one entity is oriented toward and directly opposite another entity, such that it is facing it.
-
C.
hasFace
Indicates that one entity possesses, displays, or is characterized by a face.
-
D.
changedFrom
Indicates that an entity previously had one state, value, or form and was altered or updated from that prior condition to a new one.
-
E.
defacedWith
Indicates that one entity has been damaged, marred, or vandalized using another entity as the means or material of defacement.
- 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_69c6888227bc8190a1394679e3116f90 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e6493fd88190b0c066a2ad74917c |
completed | March 27, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c7289881909f3b533c384f9ed4 |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e4a15b088190bee9a23e94aaac53 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:44 p.m.