Triple
T9274525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hutchison |
E222911
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Kay Bailey Hutchison |
E43128
|
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: Kay Bailey Hutchison | Statement: [Hutchison, hasNotableBearer, Kay Bailey Hutchison]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kay Bailey Hutchison Context triple: [Hutchison, hasNotableBearer, Kay Bailey Hutchison]
-
A.
Kay Bailey Hutchison
chosen
Kay Bailey Hutchison is an American attorney and Republican politician who served as a long-time U.S. Senator from Texas.
-
B.
Cynthia Lummis
Cynthia Lummis is a Republican politician from Wyoming who has served as both a U.S. representative and the state's first female U.S. senator.
-
C.
Harriet Hageman
Harriet Hageman is a Republican politician and attorney from Wyoming who serves as the state's at-large U.S. Representative after defeating Liz Cheney in a high-profile 2022 primary.
-
D.
Kim Roth
Kim Roth is a film producer best known for her work on critically acclaimed dramas such as "Mudbound."
-
E.
Mari Blanchard
Mari Blanchard was an American film and television actress of the 1950s and 1960s, known for her roles in Westerns and adventure films.
- 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_69ca841ffe208190aa7bcffbef2f8379 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd078a045c8190b2c4d1ec64b932ad |
completed | April 1, 2026, 11:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d09c3dd140819099fc0e95c4d48ba9 |
completed | April 4, 2026, 5:06 a.m. |
Created at: March 30, 2026, 7:33 p.m.