Triple
T37011179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuck Hansen |
E915949
|
entity |
| Predicate | competesForLoveInterest |
P115860
|
FINISHED |
| Object | Lauren Scott |
—
|
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: Lauren Scott | Statement: [Tuck Hansen, competesForLoveInterest, Lauren Scott]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: competesForLoveInterest Context triple: [Tuck Hansen, competesForLoveInterest, Lauren Scott]
-
A.
romanticRivalryWith
chosen
Indicates a mutual competitive relationship in which two entities vie for the romantic attention or affection of the same person.
-
B.
loveInterestPortrayedBy
Indicates that a character’s romantic interest is depicted or played by a particular actor or performer.
-
C.
loveInterest
Indicates that one entity is the romantic object of affection or attraction for another entity.
-
D.
competesForAffectionWith
Indicates a relationship where two or more entities vie against each other to gain the affection or emotional favor of the same target entity.
-
E.
hasFictionalRomanticInterest
Indicates that one entity is portrayed as having a romantic attraction or interest toward another entity within a fictional context.
- F. None of above.
Provenance (3 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_69f76e90ed548190b187d2475f5c807d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd3d46d1f48190a1b20dd063224b7d |
completed | May 8, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69fd3ae1510c81908fe1280efc17feee |
completed | May 8, 2026, 1:22 a.m. |
Created at: May 3, 2026, 4:14 p.m.