Triple
T16432717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Genesis 27 |
E399107
|
entity |
| Predicate | containsCharacterAction |
P122761
|
FINISHED |
| Object | Rebekah prepares food for Isaac |
—
|
LITERAL 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: Rebekah prepares food for Isaac | Statement: [Genesis 27, containsCharacterAction, Rebekah prepares food for Isaac]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsCharacterAction Context triple: [Genesis 27, containsCharacterAction, Rebekah prepares food for Isaac]
-
A.
containsCharacter
Indicates that one entity includes a specific character as part of its content or composition.
-
B.
characterCorrespondsTo
Indicates that one character is equivalent to, maps onto, or represents another character in a defined correspondence or mapping.
-
C.
capturesCharacter
Indicates that one entity seizes, traps, or takes control of another character.
-
D.
hasRecurringCharacterFrom
Indicates that one work or series includes a character who also appears recurrently in another work or series.
-
E.
hasSpecialCharacter
Indicates that a given entity (such as a string or identifier) contains at least one non-alphanumeric special character.
- F. None of above. chosen
Provenance (4 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32b9f2e8c81909c60b8fb78255e5f |
completed | April 18, 2026, 6:58 a.m. |
| PD | Predicate disambiguation | batch_69e22701d2288190bf8676050758f172 |
completed | April 17, 2026, 12:26 p.m. |
| PDg | Predicate description generation | batch_69e24556c1348190902a4d116c3137d9 |
completed | April 17, 2026, 2:36 p.m. |
Created at: April 10, 2026, 5:10 a.m.