Triple
T36784119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laura Dern as Diana |
E908853
|
entity |
| Predicate | relationshipTone |
P205044
|
FINISHED |
| Object | tender |
—
|
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: tender | Statement: [Laura Dern as Diana, relationshipTone, tender]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipTone Context triple: [Laura Dern as Diana, relationshipTone, tender]
-
A.
relationshipDynamic
Indicates a changing or evolving pattern of interaction between entities, such as shifts in their roles, closeness, or influence over time.
-
B.
relationshipImpact
Indicates how one entity’s relationship with another affects or changes those entities or their interaction.
-
C.
relationshipType
Indicates the specific kind of relationship that exists between two or more entities.
-
D.
relationshipFocus
Indicates a relationship where particular attention, priority, or emphasis is placed on the connection between two or more entities.
-
E.
relationshipNote
Indicates that there is an associated note or annotation describing or qualifying the relationship between the entities.
- 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_69f76e7a937c81909ed7359641e670f6 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a0e039481908a4a2666f76c5363 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:12 p.m.