Triple
T2134263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Skoll |
E46613
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jeffrey |
E61391
|
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: Jeffrey | Statement: [Jeff Skoll, givenName, Jeffrey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Context triple: [Jeff Skoll, givenName, Jeffrey]
-
A.
Jeffrey
chosen
Jeffrey is a masculine given name of Germanic origin, commonly used in English-speaking countries.
-
B.
Jared
Jared is the given name of Jared Diamond, an American geographer, historian, and author best known for his Pulitzer Prize–winning book "Guns, Germs, and Steel."
-
C.
Jonathan
Jonathan is a common masculine given name of Hebrew origin, meaning "Yahweh has given."
-
D.
Gavin
Gavin is a masculine given name of Celtic origin, commonly used in English-speaking countries.
-
E.
Garrett
Garrett is a masculine given name of Old French and Germanic origin, commonly used in English-speaking countries.
- 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_69a88a174ab48190a5db20c132e5dccf |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbba24004819091ffb9440e8615fa |
completed | March 7, 2026, 5:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae58d3275481909c8d74ca7c037ddd |
completed | March 9, 2026, 5:21 a.m. |
Created at: March 4, 2026, 7:44 p.m.