Triple
T5118878
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. John Brown |
E115407
|
entity |
| Predicate | loveInterestOf |
P7325
|
FINISHED |
| Object | Tita |
E496159
|
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 | Statement: [Dr. John Brown, loveInterestOf, Tita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tita Context triple: [Dr. John Brown, loveInterestOf, Tita]
-
A.
Tita
chosen
Tita is the passionate, emotionally expressive protagonist of Laura Esquivel’s novel "Like Water for Chocolate," whose cooking magically transmits her feelings to those who eat her food.
-
B.
Yerma
Yerma is a tragic play by Spanish dramatist Federico García Lorca that explores themes of infertility, honor, and societal pressure in rural Spain.
-
C.
Eva Luna
Eva Luna is a novel by Chilean author Isabel Allende that follows the imaginative life story of a young Latin American woman against a backdrop of political and social upheaval.
-
D.
Julita
Julita is a feminine given name, commonly used as a diminutive or variant of Julia in various languages and cultures.
-
E.
Marita
Marita is a feminine given name commonly used as a diminutive or affectionate form of the name Marie in various European languages.
- 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_69bd4442ade0819087b9461f892b206b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd77cf6590819081488b739efae32c |
completed | March 20, 2026, 4:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becfcf12448190a196e9397958fbba |
completed | March 21, 2026, 5:05 p.m. |
Created at: March 20, 2026, 1:42 p.m.