Triple
T13728560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bootstrap Bill Turner |
E329728
|
entity |
| Predicate | relationshipToElizabethSwann |
P111323
|
FINISHED |
| Object | father-in-law |
—
|
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: father-in-law | Statement: [Bootstrap Bill Turner, relationshipToElizabethSwann, father-in-law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToElizabethSwann Context triple: [Bootstrap Bill Turner, relationshipToElizabethSwann, father-in-law]
-
A.
relationshipStatusWithJackSparrow
Indicates the type or state of a subject’s personal relationship with Jack Sparrow.
-
B.
relationshipToEsmeralda
Indicates the specific type of relationship or connection an entity has to Esmeralda.
-
C.
MaryRelationshipToElizabeth
Indicates a relational connection or association that Mary has toward Elizabeth, without specifying the exact nature of that relationship.
-
D.
relationshipToHuck
Indicates the specific type of personal or social relationship that one entity has with Huck.
-
E.
relationshipToSamanthaGrimm
Indicates the specific type of relationship or connection an entity has to Samantha Grimm.
- 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_69d80772315881908f980cae40d91664 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69de01f746cc8190abde237bbb7e6c78 |
completed | April 14, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69dbbe92d77c81908e0244cffb7f78c5 |
completed | April 12, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69dbc59ca1a88190a6abd3bd00554c93 |
completed | April 12, 2026, 4:17 p.m. |
Created at: April 9, 2026, 9:55 p.m.