Triple
T17026089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martial Law |
E413066
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Arsenio Hall |
E248299
|
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: Arsenio Hall | Statement: [Martial Law, starring, Arsenio Hall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arsenio Hall Context triple: [Martial Law, starring, Arsenio Hall]
-
A.
Arsenio Hall
chosen
Arsenio Hall is an American comedian, actor, and talk show host best known for hosting "The Arsenio Hall Show" and for his roles in films like "Coming to America."
-
B.
Allen Ludden
Allen Ludden was an American television personality and game show host best known for hosting the quiz show "Password."
-
C.
Dan Hartman
Dan Hartman was an American musician, singer, songwriter, and producer best known for his disco and pop hits in the 1970s and 1980s.
-
D.
John Carson
John Carson is a fictional character appearing in the film "The Miracle Woman."
-
E.
William Maher
William Maher is an American comedian, political commentator, and television host best known for his satirical talk show "Real Time with Bill Maher."
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d5d46a5081908bc5681621dd8534 |
completed | April 18, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b53306081908972a0a5db474b6c |
completed | May 10, 2026, 11:57 p.m. |
Created at: April 10, 2026, 5:33 a.m.