Triple
T19312351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deborah Moore |
E483002
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Geoffrey Moore |
—
|
NE NERFINISHED |
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: Geoffrey Moore | Statement: [Deborah Moore, relative, Geoffrey Moore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Geoffrey Moore Context triple: [Deborah Moore, relative, Geoffrey Moore]
-
A.
Geoffrey Moore
chosen
Geoffrey Moore is a British actor and producer, best known as the son of James Bond star Roger Moore.
-
B.
Paul Saffo
Paul Saffo is a technology forecaster and futurist known for his work on long-term trends in science, technology, and society.
-
C.
Clayton Christensen
Clayton Christensen was an influential American business scholar and Harvard Business School professor best known for developing the theory of disruptive innovation.
-
D.
John Seely Brown
John Seely Brown is an American researcher and former chief scientist at Xerox PARC known for his influential work on organizational learning, innovation, and the social dimensions of technology.
-
E.
Tim O'Reilly
Tim O'Reilly is a prominent technology publisher, author, and founder of O'Reilly Media, known for popularizing terms like "open source" and "Web 2.0" and for his influential role in shaping the modern tech industry.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d04d5c8190baa816986f2b1d1e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e604ce5de081909811c49f56ba94bb |
completed | April 20, 2026, 10:49 a.m. |
Created at: April 10, 2026, 1:32 p.m.