Triple
T28566182
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rivka David |
E722683
|
entity |
| Predicate | relationshipToTaliDavid |
P201761
|
FINISHED |
| Object | mother |
—
|
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: mother | Statement: [Rivka David, relationshipToTaliDavid, mother]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToTaliDavid Context triple: [Rivka David, relationshipToTaliDavid, mother]
-
A.
relationshipToEliDavid
Indicates the specific familial, professional, or personal relationship that an entity has with Eli David.
-
B.
relationshipToZivaDavid
Indicates the specific interpersonal or familial connection that an entity has to Ziva David.
-
C.
relationshipToDavidPalmer
Indicates the specific nature of the connection or association that an entity has with David Palmer.
-
D.
hasRelationshipTypeWith Tai Frasier
Indicates that there exists a specific type of relationship between an entity and Tai Frasier.
-
E.
relationshipToTinaBordereau
Indicates the specific type of personal or professional relationship an entity has with Tina Bordereau.
- 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_69f01a5f69d08190ad5c0d2167078dec |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_6a001cc0ff588190bb7c8a6fd427d02b |
completed | May 10, 2026, 5:50 a.m. |
| PD | Predicate disambiguation | batch_6a001b3ea18c8190aeda7a32b2697490 |
completed | May 10, 2026, 5:44 a.m. |
| PDg | Predicate description generation | batch_6a001cc053ac8190927768a4ecb023b9 |
completed | May 10, 2026, 5:50 a.m. |
Created at: April 28, 2026, 4:07 a.m.