Triple
T13377488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Laura Hager |
E319225
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Hager |
E313855
|
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: Hager | Statement: [Margaret Laura Hager, familyName, Hager]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hager Context triple: [Margaret Laura Hager, familyName, Hager]
-
A.
Hager
chosen
Hager is the surname of Jenna Bush Hager, an American television personality, author, and daughter of former U.S. President George W. Bush.
-
B.
Hubbell
Hubbell is a surname most famously associated with Carl Hubbell, a Hall of Fame Major League Baseball pitcher known for his dominant screwball in the 1930s.
-
C.
Rheem
Rheem is a major American manufacturer of heating, cooling, water heating, and pool/spa heating products.
-
D.
Schneider
Schneider is a German-origin surname commonly borne by people of German-speaking or Central European descent.
-
E.
Eaton’s
Eaton’s was a major Canadian department store chain that became a retail icon and helped shape downtown shopping districts across the country.
- 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_69d806b886bc8190b676e7768b8e01c5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadce3fec48190a5443d87c85477a3 |
completed | April 11, 2026, 11:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f726893a8c8190b26b5f7c7fb96f1f |
completed | May 3, 2026, 10:42 a.m. |
Created at: April 9, 2026, 9:33 p.m.