Triple
T22347843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luc Teyssier |
E552440
|
entity |
| Predicate | romanticInterest |
P7325
|
FINISHED |
| Object | Kate |
—
|
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: Kate | Statement: [Luc Teyssier, romanticInterest, Kate]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Context triple: [Luc Teyssier, romanticInterest, Kate]
-
A.
Kate
chosen
Kate is a common diminutive form of the given name Catherine, frequently used in English-speaking countries.
-
B.
Kate
Kate is the Allied reporting name for the Nakajima B5N, a Japanese World War II carrier-based torpedo bomber aircraft.
-
C.
Anne
Anne is the protagonist of "The Darkest Hour," around whom the film’s central conflict and emotional journey revolve.
-
D.
Anne
Anne is the middle name of Elizabeth Anne Bloomer, better known as former U.S. First Lady Betty Ford.
-
E.
Anne
Anne is the central character in the short story "In the Gloaming," around whom the narrative’s emotional and thematic developments revolve.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e4a0ad08190a385b4d343cf6524 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f157995bec819080b8d05fa88704ed |
completed | April 29, 2026, 12:58 a.m. |
Created at: April 16, 2026, 8:43 p.m.