Triple
T9488150
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malakoff |
E228814
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Pastrana |
E347523
|
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: Pastrana | Statement: [Malakoff, hasTwinTown, Pastrana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pastrana Context triple: [Malakoff, hasTwinTown, Pastrana]
-
A.
Pastrana
chosen
Pastrana is a historic town in central Spain known for its well-preserved medieval architecture and association with the Dukes of Pastrana and Princess of Éboli.
-
B.
Mazariegos
Mazariegos is a Spanish-origin surname borne by various notable individuals, including figures in Latin American history and culture.
-
C.
Garzón
Garzón is a municipality and town in south-central Colombia known as an agricultural center within the Huila Department.
-
D.
Fernando Aguirre
Fernando Aguirre is a character in the 1952 biographical film "Viva Zapata!" about the Mexican revolutionary leader Emiliano Zapata.
-
E.
Fernando García
Fernando García is a common Spanish personal name shared by numerous individuals across fields such as sports, arts, and public life.
- 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_69ca847424f081908180305555139f7a |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd80c443b88190968d2092a73e1ee4 |
completed | April 1, 2026, 8:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d12d18fd908190b562fa0a8dad7c63 |
completed | April 4, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:55 p.m.