Triple
T17902779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mathew St. Patrick |
E447624
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mathew |
E264164
|
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: Mathew | Statement: [Mathew St. Patrick, givenName, Mathew]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mathew Context triple: [Mathew St. Patrick, givenName, Mathew]
-
A.
Mathew
chosen
Mathew is the given name of Mathew Knowles, the American music executive best known as Beyoncé’s father and former manager.
-
B.
John Matthew Matthan
John Matthew Matthan is an Indian film director best known for helming the acclaimed 1999 Hindi crime drama "Sarfarosh."
-
C.
Russ Matthews
Russ Matthews is a fictional character best known as one of Iris Carrington’s significant romantic partners in the long-running soap opera "Another World."
-
D.
Daniel Matthews
Daniel Matthews is a fictional character in the Saw horror film franchise, notably appearing as one of Jigsaw’s trapped victims in Saw II.
-
E.
Matthew Nathan
Matthew Nathan was a British Army officer and colonial administrator who served as Governor of Hong Kong in the early 20th century.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49e99c3188190aead24cd3d48c7a7 |
completed | April 19, 2026, 9:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a031b2a27748190b620da54b4222a37 |
completed | May 12, 2026, 12:20 p.m. |
Created at: April 10, 2026, 10:19 a.m.