Triple
T19327585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Destroy All Monsters |
E483399
|
entity |
| Predicate | member |
P10
|
FINISHED |
| Object |
Niagara
Niagara is an American singer and visual artist best known as the charismatic frontwoman of the Detroit proto-punk band Destroy All Monsters.
|
E1371766
|
NE FINISHED |
How this triple was built (4 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: Niagara | Statement: [Destroy All Monsters, member, Niagara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Niagara Context triple: [Destroy All Monsters, member, Niagara]
-
A.
Niagara
Niagara is a 1953 film noir thriller starring Marilyn Monroe, noted for its dramatic use of the Niagara Falls setting and Monroe’s breakout femme fatale performance.
-
B.
Niagara
Niagara is a small rural city located in Grand Forks County in the U.S. state of North Dakota.
-
C.
Niagara
Niagara is a cold-hardy, labrusca-based white grape variety widely grown in the eastern United States, known for its distinctive “foxy” aroma and use in sweet and table wines.
-
D.
Niagara
Niagara is the codename for Sun Microsystems' UltraSPARC T1 multicore, multithreaded server processor designed for high-throughput, low-power computing.
-
E.
Niagara
Niagara is a regional municipality in southern Ontario, Canada, known for encompassing the famous Niagara Falls and surrounding communities.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Niagara Triple: [Destroy All Monsters, member, Niagara]
Generated description
Niagara is an American singer and visual artist best known as the charismatic frontwoman of the Detroit proto-punk band Destroy All Monsters.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Niagara Target entity description: Niagara is an American singer and visual artist best known as the charismatic frontwoman of the Detroit proto-punk band Destroy All Monsters.
-
A.
Niagara
Niagara is a 1953 film noir thriller starring Marilyn Monroe, noted for its dramatic use of the Niagara Falls setting and Monroe’s breakout femme fatale performance.
-
B.
Niagara
Niagara is a small rural city located in Grand Forks County in the U.S. state of North Dakota.
-
C.
Niagara
Niagara is a cold-hardy, labrusca-based white grape variety widely grown in the eastern United States, known for its distinctive “foxy” aroma and use in sweet and table wines.
-
D.
Niagara
Niagara is a regional municipality in southern Ontario, Canada, known for encompassing the famous Niagara Falls and surrounding communities.
-
E.
Niagara
Niagara is the codename for Sun Microsystems' UltraSPARC T1 multicore, multithreaded server processor designed for high-throughput, low-power computing.
- F. None of above. chosen
Provenance (5 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_69d8e8d13e3c81909d91d1d5ec37c095 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e6163e3a5081909195192356bcebc3 |
completed | April 20, 2026, 12:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0724019bac8190bd3034d044336492 |
completed | May 15, 2026, 1:47 p.m. |
| NEDg | Description generation | batch_6a0725d467cc8190b42352c1b591bc6f |
completed | May 15, 2026, 1:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0726f886d48190bca4b40b17e6c46b |
completed | May 15, 2026, 2 p.m. |
Created at: April 10, 2026, 1:33 p.m.