Triple
T899328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Book of Judges |
E19411
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object |
Deborah
Deborah is a prominent biblical prophetess and judge of Israel known for her leadership and role in delivering the Israelites from Canaanite oppression.
|
E109288
|
NE FINISHED |
How this triple was built (4 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: Deborah | Statement: [Book of Judges, featuresCharacter, Deborah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deborah Context triple: [Book of Judges, featuresCharacter, Deborah]
-
A.
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.
-
B.
Miryam
Miryam is the Hebrew form of the name of the Virgin Mary, the mother of Jesus in Christian tradition.
-
C.
Susanna
Susanna is a deuterocanonical addition to the Book of Daniel, telling the story of a virtuous woman falsely accused of adultery and vindicated by the prophet Daniel.
-
D.
Tessie
Tessie is a Boston Red Sox mascot character, often depicted as a green monster and associated with Wally the Green Monster.
-
E.
Ruth
Ruth is a supporting character in the comedy Western film "A Million Ways to Die in the West," known for being a devout Christian prostitute engaged to the protagonist's best friend.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Deborah Triple: [Book of Judges, featuresCharacter, Deborah]
Generated description
Deborah is a prominent biblical prophetess and judge of Israel known for her leadership and role in delivering the Israelites from Canaanite oppression.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Deborah Target entity description: Deborah is a prominent biblical prophetess and judge of Israel known for her leadership and role in delivering the Israelites from Canaanite oppression.
-
A.
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.
-
B.
Miryam
Miryam is the Hebrew form of the name of the Virgin Mary, the mother of Jesus in Christian tradition.
-
C.
Susanna
Susanna is a deuterocanonical addition to the Book of Daniel, telling the story of a virtuous woman falsely accused of adultery and vindicated by the prophet Daniel.
-
D.
Tessie
Tessie is a Boston Red Sox mascot character, often depicted as a green monster and associated with Wally the Green Monster.
-
E.
Ruth
Ruth is the given name of Ruth Bader Ginsburg, the pioneering U.S. Supreme Court Justice and prominent advocate for gender equality and civil rights.
- F. None of above. chosen
Provenance (5 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_69a4939e889c8190ac148b3ac1a7f90b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ad4162848190aa2787b2fa3e6575 |
completed | March 1, 2026, 9:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7ee02a21c819088e6a137ea306efd |
completed | March 4, 2026, 8:32 a.m. |
| NEDg | Description generation | batch_69a7eebc724c8190a364782ec8e672e0 |
completed | March 4, 2026, 8:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a7ef2ed2c881909dc1d2d6fb39805a |
completed | March 4, 2026, 8:37 a.m. |
Created at: March 1, 2026, 7:39 p.m.