Triple
T20400621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Standing Fast |
E500320
|
entity |
| Predicate | coAuthor |
P398
|
FINISHED |
| Object | Tom Mathews |
—
|
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: Tom Mathews | Statement: [Standing Fast, coAuthor, Tom Mathews]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Mathews Context triple: [Standing Fast, coAuthor, Tom Mathews]
-
A.
Tom Mathews
chosen
Tom Mathews is an American writer and journalist known for co-authoring civil rights leader Roy Wilkins’s autobiography, "Standing Fast."
-
B.
William Mathews
William Mathews was a 19th-century British mountaineer notable for pioneering ascents in the Alps.
-
C.
Richard Mathews
Richard Mathews is an actor known for playing the Time Lord Rassilon in the long-running British science fiction series Doctor Who.
-
D.
John Matthews
John Matthews is a relatively common personal name shared by numerous individuals across various professions and public roles.
-
E.
Peter Mathews
Peter Mathews is a prominent scholar of Maya hieroglyphic writing, known for his influential contributions to the decipherment and understanding of Maya epigraphy.
- 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_69e0b4a81bec8190b69adfdc1336a015 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6798db79c819085e3a2526538726f |
completed | April 20, 2026, 7:07 p.m. |
Created at: April 16, 2026, 11:29 a.m.