Triple
T14123382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cliff Fletcher |
E339958
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Fletcher |
E45150
|
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: Fletcher | Statement: [Cliff Fletcher, familyName, Fletcher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fletcher Context triple: [Cliff Fletcher, familyName, Fletcher]
-
A.
Fletcher
chosen
Fletcher is a surname of English origin borne by numerous notable individuals across fields such as the military, politics, arts, and sports.
-
B.
Morison
Morison is a surname most notably associated with Samuel Eliot Morison, the Pulitzer Prize–winning American historian and naval officer.
-
C.
Anson
Anson is a masculine given name of English origin, historically borne by figures such as Anson Jones, the last president of the Republic of Texas.
-
D.
Anson
Anson is a commercial office building located in the Downtown Core of Singapore’s central business district.
-
E.
Admiral Grant
Admiral Grant is a fictional high-ranking naval officer portrayed by actor John Amos.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de6095548881908a9e66adccca92d2 |
completed | April 14, 2026, 3:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcdf07feb48190b7519204b4f789b4 |
completed | May 7, 2026, 6:50 p.m. |
Created at: April 9, 2026, 10:22 p.m.