Triple
T13687853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catalina |
E328177
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Lina |
E757413
|
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: Lina | Statement: [Catalina, hasShortForm, Lina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lina Context triple: [Catalina, hasShortForm, Lina]
-
A.
Lina
Lina is a Native American servant in Toni Morrison’s novel *A Mercy*, whose history of displacement and resilience reflects the novel’s themes of slavery, colonialism, and survival in 17th-century America.
-
B.
Lina
Lina Heydrich was the wife of high-ranking Nazi official Reinhard Heydrich and a committed supporter of National Socialism in Germany.
-
C.
Lina
chosen
Lina is a feminine given name used in various cultures, often as a short form of names like Angelina, Karolina, or Alina.
-
D.
Lina
Lina is one of the four main playable characters in the Japanese video game *Yume Kōjō: Doki Doki Panic*, which later served as the basis for *Super Mario Bros. 2* in the West.
-
E.
Lina
Lina is a female lion character who serves as one of the official mascots for the Japanese professional baseball team Saitama Seibu Lions.
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc670968881908e2b4fdf656c7285 |
completed | April 12, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d4c52fc8190a93d05c24a8d1513 |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:53 p.m.