Triple
T8534570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Willis Ambler |
E202045
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Ambler |
E132747
|
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: Ambler | Statement: [Mary Willis Ambler, familyName, Ambler]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ambler Context triple: [Mary Willis Ambler, familyName, Ambler]
-
A.
Ambler
chosen
Ambler is a small Inupiat community and city in northwestern Alaska, located along the Kobuk River above the Arctic Circle.
-
B.
Newburgh
Newburgh is a small historic town in eastern Scotland known for its scenic setting on the River Tay and its traditional Scottish character.
-
C.
Newburgh
Newburgh is a small rural village in Lancashire, England, known for its historic buildings and scenic countryside setting.
-
D.
Langhorne
Langhorne is the middle name of Samuel Langhorne Clemens, better known by his pen name Mark Twain, the famed American author and humorist.
-
E.
Langhorne
Langhorne is a surname most notably associated with Crystal Langhorne, an American professional basketball player who starred in the WNBA.
- 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_69ca832355b08190b8b6a4ab4a4a3554 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6a0ccd4819097b41d0dfb1c5018 |
completed | March 31, 2026, 3:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6d7aa3a881909bfdd3536dbc4ec7 |
completed | April 2, 2026, 1:22 p.m. |
Created at: March 30, 2026, 6:17 p.m.