Triple
T3805079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosaura |
E91785
|
entity |
| Predicate | conflictsWith |
P4897
|
FINISHED |
| Object | Tita de la Garza |
E388925
|
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: Tita de la Garza | Statement: [Rosaura, conflictsWith, Tita de la Garza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tita de la Garza Context triple: [Rosaura, conflictsWith, Tita de la Garza]
-
A.
Tita de la Garza
Tita de la Garza is the passionate, emotionally expressive heroine of Laura Esquivel’s novel "Like Water for Chocolate," whose cooking magically channels her feelings.
-
B.
Mariquita
Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
-
C.
Tita De la Garza
chosen
Tita De la Garza is the passionate, magically gifted protagonist of Laura Esquivel’s novel "Like Water for Chocolate," whose emotions infuse the food she cooks.
-
D.
Blanca
Blanca is a feminine given name, common in Spanish-speaking cultures, that corresponds to the English and French name Blanche.
-
E.
Haydée
Haydée is a fictional Greek princess and former slave who becomes a devoted ally and love interest of Edmond Dantès in Alexandre Dumas' novel "The Count of Monte Cristo."
- 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_69aed96354f48190a768966d6bd19b04 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee7bd95e881908649ce512f7470b0 |
completed | March 9, 2026, 3:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51c70a98081908e86293f7e684649 |
completed | March 14, 2026, 8:29 a.m. |
Created at: March 9, 2026, 3:15 p.m.