Triple
T12826066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | León–Silao industrial corridor |
E306654
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Silao |
E811920
|
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: Silao | Statement: [León–Silao industrial corridor, nearCity, Silao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silao Context triple: [León–Silao industrial corridor, nearCity, Silao]
-
A.
Silao
chosen
Silao is a city in the Mexican state of Guanajuato known as an industrial and transportation hub in the Bajío region.
-
B.
Surigaonon
Surigaonon is a Visayan language spoken primarily in the Caraga region of northeastern Mindanao in the Philippines.
-
C.
Cabiao
Cabiao is a municipality in the province of Nueva Ecija in the Philippines, known for its agricultural economy and rural communities.
-
D.
Malpaso
Malpaso is the highest peak on the Canary Island of El Hierro, known for its panoramic views over the island and surrounding Atlantic Ocean.
-
E.
San Francisco de Dilao
San Francisco de Dilao is a historical namesake associated with Spanish colonial-era fortifications in Manila, Philippines.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96facb2d48190bc12efc00c9da360 |
completed | April 10, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f69b99d9bc8190b67f73985c8f6768 |
completed | May 3, 2026, 12:49 a.m. |
Created at: April 9, 2026, 5:33 p.m.