Triple
T10297978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salvador Alvarado |
E241546
|
entity |
| Predicate | seat |
P75
|
FINISHED |
| Object | Guamúchil |
E856081
|
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: Guamúchil | Statement: [Salvador Alvarado, seat, Guamúchil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guamúchil Context triple: [Salvador Alvarado, seat, Guamúchil]
-
A.
Guamúchil
chosen
Guamúchil is a city in the Mexican state of Sinaloa known as a regional commercial and agricultural center.
-
B.
Tuxpan
Tuxpan is a town and municipality in the Mexican state of Nayarit, known for its agricultural economy and traditional cultural festivals.
-
C.
Tuxpan
Tuxpan is a port city in the Mexican state of Veracruz, known for its Gulf Coast location and historical role as a departure point in the Cuban Revolution.
-
D.
Tototlán
Tototlán is a municipality and town in the Mexican state of Jalisco, known for its agricultural economy and traditional regional culture.
-
E.
Poncitlán
Poncitlán is a municipality and town in the Mexican state of Jalisco, known for its agricultural activity and proximity to Lake Chapala.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2ed50908190962f0d6d049fb964 |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d75018383481909abbba8247a93f8e |
completed | April 9, 2026, 7:07 a.m. |
Created at: April 6, 2026, 11:43 a.m.