Triple
T707403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary |
E14130
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Joseph |
E8383
|
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: Joseph | Statement: [Mary, spouse, Joseph]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joseph Context triple: [Mary, spouse, Joseph]
-
A.
Joseph
Joseph is the first name of J. C. R. Licklider, a pioneering computer scientist often regarded as a key figure in the development of the internet and interactive computing.
-
B.
Joseph
chosen
Joseph is the husband of Mary in the New Testament and the earthly guardian of Jesus, venerated in Christianity as a model of humility, obedience, and fatherhood.
-
C.
Joseph
Joseph is the given name of the French mathematician and physicist Jean-Baptiste Joseph Fourier, known for developing Fourier analysis and Fourier series.
-
D.
Joseph
Joseph is the full given name of American sportscaster Joe Buck, known for his play-by-play announcing of major NFL and MLB games.
-
E.
Joseph
Joseph is the given name of Joe Fulks, an early professional basketball star often credited as one of the NBA’s first great scorers.
- 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a5477bb48190a2032edc83a24720 |
completed | March 1, 2026, 8:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7927e57d48190a9e1f34c39501680 |
completed | March 4, 2026, 2:01 a.m. |
Created at: March 1, 2026, 7:36 p.m.