Triple
T4455992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alan Arkin |
E97723
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Catch-22 |
E279483
|
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: Catch-22 | Statement: [Alan Arkin, notableWork, Catch-22]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Catch-22 Context triple: [Alan Arkin, notableWork, Catch-22]
-
A.
Catch-22
chosen
Catch-22 is Joseph Heller’s satirical World War II novel that coined the term “catch-22” to describe absurd, self-contradictory bureaucratic rules and no-win situations.
-
B.
Slaughterhouse-Five
Slaughterhouse-Five is Kurt Vonnegut’s darkly comic, anti-war science fiction novel that follows Billy Pilgrim’s disjointed experiences of World War II and time travel, widely regarded as a classic of 20th-century American literature.
-
C.
Gravity’s Rainbow
Gravity’s Rainbow is a dense, postmodern novel by Thomas Pynchon that intertwines World War II-era espionage, science, and paranoia in a sprawling, experimental narrative.
-
D.
Heller
The Heller is a river in Germany that serves as a right-bank tributary of the Sieg.
-
E.
Heller
Heller is a surname shared by various notable individuals across fields such as film, literature, science, and politics.
- 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_69b3454777808190b78aa9047ba1f018 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3564216b081908c41109100b36862 |
completed | March 13, 2026, 12:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6282743e08190a2c84f3b80c6d260 |
completed | March 15, 2026, 3:31 a.m. |
Created at: March 12, 2026, 11:33 p.m.