Triple
T14880004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jack Maple |
E349973
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Jack Maple |
E349973
|
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: Jack Maple | Statement: [Jack Maple, fullName, Jack Maple]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jack Maple Context triple: [Jack Maple, fullName, Jack Maple]
-
A.
Jack Maple
chosen
Jack Maple was an influential New York City transit police officer and crime strategist best known for co-developing the CompStat system that transformed modern policing.
-
B.
Maples
Maples is the surname of Marla Maples, an American actress and television personality best known as the second wife of former U.S. President Donald Trump.
-
C.
Birch
Birch is a masculine given name most notably borne by American politician Birch Bayh, a long-serving U.S. senator from Indiana.
-
D.
Ahorn
Ahorn is a municipality in the Bavarian region of Germany, known for its rural character and proximity to the city of Coburg.
-
E.
Nyssa
Nyssa is a companion of the Fifth Doctor in the long-running British science fiction television series Doctor Who.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e622388190b2bf91cd10b9821d |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b591f3c81909ea8a9217d96e0d2 |
completed | May 8, 2026, 11:01 p.m. |
Created at: April 10, 2026, 1:55 a.m.