Triple
T1191543
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vincente Minnelli |
E25371
|
entity |
| Predicate | directed |
P7373
|
FINISHED |
| Object | Kismet |
E136799
|
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: Kismet | Statement: [Vincente Minnelli, directed, Kismet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kismet Context triple: [Vincente Minnelli, directed, Kismet]
-
A.
Kismet
Kismet is an open-source wireless network detector, sniffer, and intrusion detection system widely used for Wi-Fi security auditing and monitoring.
-
B.
Kismet
chosen
Kismet is a 1955 MGM musical fantasy film directed by Vincente Minnelli, adapted from the Broadway musical set in a stylized, exoticized Baghdad.
-
C.
Cozmo
Cozmo is the playful, costumed alien mascot of Major League Soccer club LA Galaxy, known for entertaining fans at matches and team events.
-
D.
Calliope
Calliope is the Muse of epic poetry in Greek mythology, often depicted as the chief of the nine Muses and associated with eloquence and heroic verse.
-
E.
Tikkana
Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
- 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd74e2c08190b4a48425f94addaa |
completed | March 1, 2026, 10:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac7f36371c8190a656463a9ae6402a |
completed | March 7, 2026, 7:40 p.m. |
Created at: March 1, 2026, 7:45 p.m.