Triple
T11166378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Need to Know |
E264169
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | David Sprecher |
E908664
|
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: David Sprecher | Statement: [Need to Know, writer, David Sprecher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Sprecher Context triple: [Need to Know, writer, David Sprecher]
-
A.
David Sprecher
chosen
David Sprecher is an author known for his work related to streets and urban environments.
-
B.
Ben Sprecher
Ben Sprecher is a Broadway theatre producer known for his involvement in major stage productions in New York.
-
C.
Michael Wittenberg
Michael Wittenberg was an investment adviser best known as the late husband of Broadway star Bernadette Peters.
-
D.
Daniel Kraft
Daniel Kraft is a physician-scientist, inventor, and healthcare entrepreneur known for his work in medical innovation and digital health technologies.
-
E.
Neil Hamburger
Neil Hamburger is the alter ego of American comedian Gregg Turkington, known for his deliberately awkward, anti-comedy stand-up persona and offbeat comedy albums.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e88843cc81909e503f0921c6d297 |
completed | April 9, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e483747ba88190aa6ef9df2545b18b |
completed | April 19, 2026, 7:25 a.m. |
Created at: April 8, 2026, 9:29 p.m.