Triple
T17152291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Pendergraph |
E416250
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jeff |
E162857
|
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: Jeff | Statement: [Jeff Pendergraph, givenName, Jeff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeff Context triple: [Jeff Pendergraph, givenName, Jeff]
-
A.
Jeff
Jeff is a supporting gangster character in the British crime film "The Long Good Friday," involved in the criminal underworld surrounding London mob boss Harold Shand.
-
B.
Jeff
chosen
Jeff is a masculine given name commonly used in English-speaking countries, often as a short form of Jeffrey or Jefferson.
-
C.
Jeff
Jeff is a character who appears in the Doctor Who episode "The Eleventh Hour."
-
D.
Jay
Jay is the surname of John Jay, a prominent American Founding Father and the first Chief Justice of the United States.
-
E.
Jay
Jay is a small town located in Santa Rosa County in the northwestern part of Florida.
- 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_69d886d279c081909f8ff1f743ddeb69 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f40861e08190bad1a3ec87691132 |
completed | April 18, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0148337a348190b8739eb3f553f1d9 |
completed | May 11, 2026, 3:08 a.m. |
Created at: April 10, 2026, 5:36 a.m.