Triple
T6065819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beatriz Milhazes |
E135154
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Gamboa |
E213278
|
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: Gamboa | Statement: [Beatriz Milhazes, notableWork, Gamboa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gamboa Context triple: [Beatriz Milhazes, notableWork, Gamboa]
-
A.
Gamboa
chosen
Gamboa is a small town in Panama best known for its location along the Panama Canal and its proximity to the surrounding rainforest and canal infrastructure.
-
B.
Baquero
Baquero is a Spanish surname most notably associated with actress Ivana Baquero, known for her role in the film "Pan's Labyrinth."
-
C.
O’Donojú
O’Donojú is the surname of Juan O’Donojú, the last Spanish political chief of New Spain who played a key role in Mexico’s transition to independence.
-
D.
Herrero
Herrero is a Spanish occupational surname derived from the word for "blacksmith" or "smith."
-
E.
San Simon
San Simon is a municipality in the province of Pampanga in the Philippines, known for its agricultural economy and proximity to major urban centers in Central Luzon.
- 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_69c00878d06881909ee78e88913bf890 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0573df7508190bbb5b496188b2f3e |
completed | March 22, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d23dca8819080702ca0f05df5dd |
completed | March 23, 2026, 10:59 a.m. |
Created at: March 22, 2026, 4:10 p.m.