Triple
T2146947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doug Koop |
E47088
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Koop |
E7875
|
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: Koop | Statement: [Doug Koop, hasSurname, Koop]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koop Context triple: [Doug Koop, hasSurname, Koop]
-
A.
Koop
chosen
Koop is a surname most prominently associated with C. Everett Koop, the influential former Surgeon General of the United States.
-
B.
Koops
Koops is a timid Koopa Troopa character and party member from the video game "Paper Mario: The Thousand-Year Door."
-
C.
Koopmans
Koopmans is a Dutch surname most notably associated with Nobel Prize–winning economist Tjalling C. Koopmans.
-
D.
Koopie Koo
Koopie Koo is a minor character in the Paper Mario series, known as Koops' affectionate and supportive Koopa girlfriend from his hometown.
-
E.
Kiddyland
Kiddyland is a children’s amusement area within Playland Park featuring kid-friendly rides and attractions.
- 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_69a88a1933e0819094f18426ed74180f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe25d8848190a3ba691668a68e37 |
completed | March 7, 2026, 5:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae71af0fa081909dd4b2a7452623da |
completed | March 9, 2026, 7:07 a.m. |
Created at: March 4, 2026, 7:44 p.m.