Triple
T9617088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linguini |
E232244
|
entity |
| Predicate | loveInterest |
P7325
|
FINISHED |
| Object | Colette Tatou |
E234677
|
NE 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: Colette Tatou | Statement: [Linguini, loveInterest, Colette Tatou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colette Tatou Context triple: [Linguini, loveInterest, Colette Tatou]
-
A.
Colette Tatou
chosen
Colette Tatou is a skilled and tough-minded French chef from Pixar's "Ratatouille" who mentors Linguini in the kitchen of Gusteau's restaurant.
-
B.
Violette Nozière
Violette Nozière is a 1978 French crime drama film by Claude Chabrol that recounts the true story of a notorious 1930s French parricide case.
-
C.
Annick Castiaux
Annick Castiaux is a Belgian academic and university leader who serves as rector of the University of Namur.
-
D.
Catherine Brelet
Catherine Brelet is a French film producer best known as the wife and longtime collaborator of acclaimed Swedish actor Max von Sydow.
-
E.
Suzanne Jolibois
Suzanne Jolibois was the wife of French phenomenologist and philosopher Maurice Merleau-Ponty.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca84867bb88190b4b57dd5a56d5691 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9aaf3a088190a00a7750c25b6c42 |
completed | April 1, 2026, 10:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18225f9508190bde23b9d2a40bccc |
completed | April 4, 2026, 9:27 p.m. |
Created at: March 30, 2026, 8:09 p.m.