Triple
T35002622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tasha Yar |
E1009720
|
entity |
| Predicate | hasRomanticEncounterWith |
P197180
|
FINISHED |
| Object | Data |
—
|
NE NERFINISHED |
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: Data | Statement: [Tasha Yar, hasRomanticEncounterWith, Data]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRomanticEncounterWith Context triple: [Tasha Yar, hasRomanticEncounterWith, Data]
-
A.
hasRomanticMisadventures
Indicates that an entity experiences a series of problematic, comical, or unsuccessful romantic relationships or encounters.
-
B.
hasAffairWith
Indicates that one entity is engaged in a secret or illicit romantic or sexual relationship with another entity, typically outside a committed partnership.
-
C.
hasBeenDatedBy
Indicates that one entity has previously been in a romantic or dating relationship with another entity.
-
D.
hasFictionalRomanticInterest
Indicates that one entity is portrayed as having a romantic attraction or interest toward another entity within a fictional context.
-
E.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two 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_69f76dcb716881909f75e4fd60ab2284 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fe7b1c506c8190869c1a22031e0571 |
completed | May 9, 2026, 12:09 a.m. |
| PD | Predicate disambiguation | batch_69fe796b2bdc8190a86980d44008f875 |
completed | May 9, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69fe7b1b3460819081172731b52ac15b |
completed | May 9, 2026, 12:08 a.m. |
Created at: May 3, 2026, 4:01 p.m.