Triple
T5813436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haydée Santamaría |
E128925
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Haydée |
E356687
|
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: Haydée | Statement: [Haydée Santamaría, givenName, Haydée]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haydée Context triple: [Haydée Santamaría, givenName, Haydée]
-
A.
Haydée
chosen
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."
-
B.
Consuelo
Consuelo is a feminine given name of Spanish origin, historically associated with figures such as American socialite Consuelo Vanderbilt.
-
C.
Tita De la Garza
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.
Mariquita
Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
-
E.
Amada Cruz
Amada Cruz is an American museum director and arts administrator known for leading major art institutions, including serving as director of the Seattle Art Museum.
- 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_69c0084788848190bcf71f6bc5d71597 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03360749481908d42fde7a74a754f |
completed | March 22, 2026, 6:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a185cc78819098c0f04e7ebfb3c4 |
completed | March 23, 2026, 2:12 a.m. |
Created at: March 22, 2026, 3:52 p.m.