Triple
T14666764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juan Guas |
E344396
|
entity |
| Predicate | basedIn |
P40
|
FINISHED |
| Object | Toledo |
E12295
|
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: Toledo | Statement: [Juan Guas, basedIn, Toledo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toledo Context triple: [Juan Guas, basedIn, Toledo]
-
A.
Toledo
chosen
Toledo is a historic Spanish city renowned for its medieval architecture, cultural heritage, and role as a major political and religious center in Spain’s history.
-
B.
Toledo
Toledo is a major city in northwestern Ohio, known as an industrial and transportation hub on the western end of Lake Erie.
-
C.
Toledo City
Toledo City is a coastal component city on the western side of Cebu Island in the Philippines, known for its mining industry and port facilities.
-
D.
Columbus
Columbus is a common Italian-origin surname most famously associated with the explorer Christopher Columbus and his descendants.
-
E.
Columbus
Columbus is a city in eastern Mississippi known for its historic architecture, role in the American Civil War, and as part of the Golden Triangle region.
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb54c69f8819080a37161deecfba8 |
completed | April 14, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fde177ced48190a448cbee1f4c75bf |
completed | May 8, 2026, 1:13 p.m. |
Created at: April 10, 2026, 1:27 a.m.