Triple
T11760378
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madam Secretary |
E279638
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Matt Mahoney |
E282952
|
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: Matt Mahoney | Statement: [Madam Secretary, character, Matt Mahoney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Mahoney Context triple: [Madam Secretary, character, Matt Mahoney]
-
A.
Matt Mahoney
chosen
Matt Mahoney is a fictional character from the political drama television series "Madame Secretary," involved in the professional and personal world surrounding the U.S. Secretary of State.
-
B.
Max Perlich
Max Perlich is an American character actor known for his offbeat, often quirky supporting roles in independent films and television series since the late 1980s.
-
C.
Martin Hinton
Martin Hinton is a notable individual recognized for sharing the surname associated with the Hinton family name.
-
D.
Christopher B. Roberts
Christopher B. Roberts is an American academic and engineer who serves as the president of Auburn University.
-
E.
Kenneth Regan
Kenneth Regan is an American mathematician and computer scientist known for his work in computational complexity theory and for his research on detecting cheating in chess.
- 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_69d6ab01038c819080714901502c84fc |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a52386708190b744746a2db37495 |
completed | April 10, 2026, 7:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f01a3dfd1081908221c8061931282b |
completed | April 28, 2026, 2:23 a.m. |
Created at: April 8, 2026, 9:41 p.m.