Triple
T13764234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Haley |
E330694
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Margaret Haley |
E330694
|
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: Margaret Haley | Statement: [Margaret Haley, name, Margaret Haley]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Margaret Haley Context triple: [Margaret Haley, name, Margaret Haley]
-
A.
Margaret Haley
chosen
Margaret Haley was an influential American educator and labor activist who championed teachers' rights and helped pioneer the modern teachers' union movement.
-
B.
Margaret Sexton
Margaret Sexton was the wife of U.S. Navy Rear Admiral William Thomas Sampson, a prominent figure in the Spanish–American War.
-
C.
Marjorie Harris
Marjorie Harris is the wife of former American pastor and author Joshua Harris, known for her role alongside him during his years in evangelical ministry.
-
D.
Margaret Greer
Margaret Greer is a notable individual who shares the surname Greer and is recognized as a distinguished bearer of that name.
-
E.
Margaret Welsh
Margaret Welsh is an American actress known for her work in film, television, and theater.
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de022690ac8190bd5410ecc659a2a7 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0025e6bc748190b8099e7559569a90 |
completed | May 10, 2026, 6:29 a.m. |
Created at: April 9, 2026, 10:10 p.m.