Triple
T17493732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peniston Lamb (MP) |
E425996
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Peniston |
—
|
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: Peniston | Statement: [Peniston Lamb (MP), givenName, Peniston]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peniston Context triple: [Peniston Lamb (MP), givenName, Peniston]
-
A.
Peniston
chosen
Peniston is a masculine given name of English origin, historically borne by several members of the British aristocracy.
-
B.
Ketcham
Ketcham is a supporting character in the 1970 Western film "Rio Lobo," which stars John Wayne.
-
C.
Denniston
Denniston is a historic former coal-mining town on New Zealand’s rugged West Coast, known for its dramatic plateau setting and the famous Denniston Incline.
-
D.
Tilghman
Tilghman is a masculine given name of English origin that has been borne by various notable American figures, including politicians and military officers.
-
E.
Tilghman
Tilghman is a surname most notably associated with Shirley M. Tilghman, a prominent molecular biologist and former president of Princeton University.
- 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_69d889dccf7481909264a1844a2e9100 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e451d782688190afb76fa080867315 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 5:48 a.m.