Triple
T3580241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary |
E75782
|
entity |
| Predicate | etymologicalForm |
P12512
|
FINISHED |
| Object | Miryam |
E4648
|
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: Miryam | Statement: [Mary, etymologicalForm, Miryam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miryam Context triple: [Mary, etymologicalForm, Miryam]
-
A.
Miryam
chosen
Miryam is the Hebrew form of the name of the Virgin Mary, the mother of Jesus in Christian tradition.
-
B.
Miriam
Miriam is a prominent biblical figure known as the sister of Moses and Aaron and as a prophetess during the Exodus of the Israelites from Egypt.
-
C.
Miriam
Miriam is a central fictional character in Nathaniel Hawthorne’s novel "The Marble Faun," portrayed as a mysterious and artistically gifted woman with a troubled past.
-
D.
Zipporah
Zipporah is the Midianite wife of Moses in the Hebrew Bible, known for accompanying him to Egypt and intervening to save his life in a mysterious incident involving their son’s circumcision.
-
E.
Bilhah
Bilhah is a biblical figure who served as Rachel’s maidservant and bore two of Jacob’s sons, Dan and Naphtali.
- 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_69ad85d5e3008190bdfe0bacdd1f5a1b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0ffecdc8190bf01c8ba90e3733e |
completed | March 8, 2026, 6:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bbc95fa881909846a6d53ba6a24e |
completed | March 13, 2026, 7:24 a.m. |
Created at: March 8, 2026, 3:21 p.m.