Triple
T13370876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Townsend Griffiss |
E319054
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Townsend |
E270517
|
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: Townsend | Statement: [Townsend Griffiss, givenName, Townsend]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Townsend Context triple: [Townsend Griffiss, givenName, Townsend]
-
A.
Townsend
chosen
Townsend is a surname of English origin borne by numerous notable individuals across fields such as politics, science, and the arts.
-
B.
Tilton
Tilton is a locality in the United Kingdom notable for lending its name to the territorial designation of the peerage title Baron Keynes of Tilton.
-
C.
Faison
Faison is a surname most notably associated with American actor and comedian Donald Faison.
-
D.
Easterbrook
Easterbrook is a surname most notably associated with American actress Leslie Easterbrook, known for her roles in the "Police Academy" film series and various television shows.
-
E.
Sibley
Sibley is a surname most notably associated with Henry Hastings Sibley, an early political and military leader in Minnesota history.
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadcd8950481909785a2060f43b6ed |
completed | April 11, 2026, 11:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f72682c7f08190b8553ca22734df27 |
completed | May 3, 2026, 10:42 a.m. |
Created at: April 9, 2026, 9:33 p.m.