Triple
T11634769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gil Robbins |
E276487
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Mary Robbins |
E469579
|
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: Mary Robbins | Statement: [Gil Robbins, spouse, Mary Robbins]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Robbins Context triple: [Gil Robbins, spouse, Mary Robbins]
-
A.
Mary Robbins
chosen
Mary Robbins is known as the mother of American actor and filmmaker Tim Robbins.
-
B.
Mary Easty
Mary Easty was a respected Salem, Massachusetts woman who was falsely accused of witchcraft and executed during the 1692 Salem witch trials, later remembered for her dignified plea for justice.
-
C.
Ruth Robbins
Ruth Robbins is an academic and author known for her work in literary and cultural studies.
-
D.
Mary Margaret Blanchard
Mary Margaret Blanchard is a central character in the TV series "Once Upon a Time," the Storybrooke schoolteacher who is actually Snow White under a curse.
-
E.
Mary Cantey
Mary Cantey was an American woman of the early 19th century best known as the wife of U.S. Senator and Vice President John C. Calhoun.
- 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_69d6aafa51148190ab84940694c00235 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a25c0b00819095898d2b2445ecfb |
completed | April 10, 2026, 7:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6d6d4bd8819087902472e77e0d38 |
completed | May 8, 2026, 4:58 a.m. |
Created at: April 8, 2026, 9:39 p.m.