Triple
T3644629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Quentin |
E77267
|
entity |
| Predicate | romanticInvolvementWith |
P7325
|
FINISHED |
| Object | a carnival worker |
—
|
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: a carnival worker | Statement: [Miss Quentin, romanticInvolvementWith, a carnival worker]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: romanticInvolvementWith Context triple: [Miss Quentin, romanticInvolvementWith, a carnival worker]
-
A.
hasRomanticTensionWith
Indicates a mutual or one-sided romantic attraction or unresolved romantic interest existing between two entities.
-
B.
spouseOrLover
Indicates a romantic partnership between two entities, whether formalized as a spouse or existing as a lover.
-
C.
loveInterest
chosen
Indicates that one entity is the romantic object of affection or attraction for another entity.
-
D.
relationshipStatusDuringFilm
Indicates the type or state of a relationship between entities specifically during the time period in which a film takes place or is produced.
-
E.
hasAffairWith
Indicates that one entity is engaged in a secret or illicit romantic or sexual relationship with another entity, typically outside a committed partnership.
- 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_69ad85de1b988190a45f8dbfebc806fc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc35c28908190b253f4835918a2b4 |
completed | March 8, 2026, 6:43 p.m. |
| PD | Predicate disambiguation | batch_69adb8445b2c8190ab07f6ad4e010d0e |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:24 p.m.