Triple
T10637099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Torres Maldonado Library |
E250615
|
entity |
| Predicate | hasNamePart |
P5298
|
FINISHED |
| Object | Torres |
E657102
|
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: Torres | Statement: [Torres Maldonado Library, hasNamePart, Torres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Torres Context triple: [Torres Maldonado Library, hasNamePart, Torres]
-
A.
Torres
chosen
Torres is a common Spanish surname borne by numerous notable figures across the Spanish-speaking world.
-
B.
Sanchez
Sanchez is a common Spanish-origin surname borne by numerous notable individuals across sports, politics, arts, and other fields.
-
C.
Carvajal
Carvajal is a Spanish surname of likely toponymic origin, borne by various notable figures in Spanish and Latin American history.
-
D.
Nando Torres
Nando Torres is a central character in the family comedy film "Yes Day," portrayed as one of the children whose parents agree to say yes to all of their requests for 24 hours.
-
E.
Ramos
Ramos is a municipality in the Philippine province of Tarlac known for its predominantly agricultural economy and rural communities.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfae51148190840a4e52b29ad06e |
completed | April 8, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96bc57a8081908abd73f4273d0666 |
completed | April 10, 2026, 9:29 p.m. |
Created at: April 8, 2026, 9:04 p.m.