Triple
T1748839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zootopia |
E38393
|
entity |
| Predicate | characterVoiced |
P13156
|
FINISHED |
| Object |
Shakira as Gazelle
Shakira as Gazelle is the pop star’s animated alter ego, a glamorous gazelle and famous singer in Disney’s film "Zootopia."
|
E197224
|
NE FINISHED |
How this triple was built (4 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: Shakira as Gazelle | Statement: [Zootopia, characterVoiced, Shakira as Gazelle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shakira as Gazelle Context triple: [Zootopia, characterVoiced, Shakira as Gazelle]
-
A.
Mariquita
Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
-
B.
Lulu
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
C.
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.
-
D.
Crazy Chick
"Crazy Chick" is a 2005 pop single by Welsh singer Charlotte Church that marked her transition from classical crossover to mainstream pop music.
-
E.
Blanca
Blanca is a feminine given name, common in Spanish-speaking cultures, that corresponds to the English and French name Blanche.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Shakira as Gazelle Triple: [Zootopia, characterVoiced, Shakira as Gazelle]
Generated description
Shakira as Gazelle is the pop star’s animated alter ego, a glamorous gazelle and famous singer in Disney’s film "Zootopia."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shakira as Gazelle Target entity description: Shakira as Gazelle is the pop star’s animated alter ego, a glamorous gazelle and famous singer in Disney’s film "Zootopia."
-
A.
Mariquita
Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
-
B.
Lulu
Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
-
C.
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.
-
D.
Crazy Chick
"Crazy Chick" is a 2005 pop single by Welsh singer Charlotte Church that marked her transition from classical crossover to mainstream pop music.
-
E.
Blanca
Blanca is a feminine given name, common in Spanish-speaking cultures, that corresponds to the English and French name Blanche.
- F. None of above. chosen
Provenance (5 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_69a8862b01a48190ab47209063af82d9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa63ee4d2081909dfd6d3244228c56 |
completed | March 6, 2026, 5:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0e21e58819082943212bd725581 |
completed | March 8, 2026, 4:16 p.m. |
| NEDg | Description generation | batch_69ada1a2fb9481909d9ed587921ca6b6 |
completed | March 8, 2026, 4:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ada4dfc9188190845a4e4490318d68 |
completed | March 8, 2026, 4:33 p.m. |
Created at: March 4, 2026, 7:31 p.m.