Triple
T24292022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gene Tierney as Laura Hunt |
E605848
|
entity |
| Predicate | relationshipTypeWithWaldoLydecker |
P155413
|
FINISHED |
| Object | protégée and love interest |
—
|
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: protégée and love interest | Statement: [Gene Tierney as Laura Hunt, relationshipTypeWithWaldoLydecker, protégée and love interest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTypeWithWaldoLydecker Context triple: [Gene Tierney as Laura Hunt, relationshipTypeWithWaldoLydecker, protégée and love interest]
-
A.
relationshipToLloyd
Indicates the specific type of personal or social relationship an entity has with Lloyd.
-
B.
hasRelationshipTypeWith Frank Drebin
Indicates that there exists a specific type of relationship between an entity and Frank Drebin.
-
C.
relationshipTypeWithReynoldsWoodcock
Indicates the specific nature or category of relationship an entity has with Reynolds Woodcock.
-
D.
relationshipTypeWith Sebastian Wilder
Indicates the specific nature or category of relationship that an entity has with Sebastian Wilder.
-
E.
relationshipTypeWith Larry Darrell
Indicates the specific type or nature of the relationship that an entity has with Larry Darrell.
- 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_69e29549335881909cbf27adcaba1cf0 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f29156ab8081909435b7178e9889bc |
completed | April 29, 2026, 11:16 p.m. |
| PD | Predicate disambiguation | batch_69f1c45c6ec081908401b69424428100 |
completed | April 29, 2026, 8:42 a.m. |
| PDg | Predicate description generation | batch_69f1c6d4e99081909f61899eccafb73e |
completed | April 29, 2026, 8:52 a.m. |
Created at: April 18, 2026, 12:09 a.m.