Triple
T15625593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike and Dave Need Wedding Dates |
E375667
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Evan Henke |
E787152
|
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: Evan Henke | Statement: [Mike and Dave Need Wedding Dates, editedBy, Evan Henke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Evan Henke Context triple: [Mike and Dave Need Wedding Dates, editedBy, Evan Henke]
-
A.
Evan Henke
chosen
Evan Henke is a film editor known for his work on the comedy movie "Office Christmas Party."
-
B.
Evan Mast
Evan Mast is an American musician and producer best known as one half of the electronic duo Ratatat and for his production work with prominent hip-hop artists.
-
C.
Evan Washburn
Evan Washburn is an American sports reporter best known as a sideline correspondent for CBS’s NFL coverage, including major events like the Super Bowl.
-
D.
Evan Martin
Evan Martin is a software engineer known for his work on the Ninja build system and contributions to large-scale C++ development tooling.
-
E.
Evan Daugherty
Evan Daugherty is an American screenwriter best known for his work on major Hollywood films such as "Snow White and the Huntsman," "Divergent," and "Teenage Mutant Ninja Turtles."
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e9e5e248190ae54cda1fde51efb |
completed | April 16, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3b205dc81908c4194a931d94074 |
completed | May 9, 2026, 11:30 p.m. |
Created at: April 10, 2026, 4:14 a.m.