Triple
T21357323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Conejos County |
E526667
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Cenicero
Cenicero is a small, unincorporated community located in rural Conejos County in southern Colorado.
|
E1480560
|
NE FINISHED |
How this triple was built (4 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: Cenicero | Statement: [Conejos County, contains, Cenicero]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cenicero Context triple: [Conejos County, contains, Cenicero]
-
A.
Valle Gómez
Valle Gómez is a Mexico City Metro station on Line 5 serving the Valle Gómez neighborhood in the city.
-
B.
Capiceño
Capiceño is an alternative name for the Capiznon language, an Austronesian language spoken primarily in the province of Capiz in the Philippines.
-
C.
Zapatoca
Zapatoca is a historic town and municipality in northeastern Colombia known for its colonial architecture, mild climate, and scenic Andean landscapes.
-
D.
Montefrío
Montefrío is a picturesque Andalusian town in southern Spain, renowned for its dramatic hilltop setting, whitewashed houses, and historic church and castle overlooking surrounding olive groves.
-
E.
Mazaleón
Mazaleón is a small municipality in the province of Teruel, Aragon, Spain, known for its rural character and location in the Matarranya comarca.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Cenicero Triple: [Conejos County, contains, Cenicero]
Generated description
Cenicero is a small, unincorporated community located in rural Conejos County in southern Colorado.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cenicero Target entity description: Cenicero is a small, unincorporated community located in rural Conejos County in southern Colorado.
-
A.
Valle Gómez
Valle Gómez is a Mexico City Metro station on Line 5 serving the Valle Gómez neighborhood in the city.
-
B.
Capiceño
Capiceño is an alternative name for the Capiznon language, an Austronesian language spoken primarily in the province of Capiz in the Philippines.
-
C.
Zapatoca
Zapatoca is a historic town and municipality in northeastern Colombia known for its colonial architecture, mild climate, and scenic Andean landscapes.
-
D.
Montefrío
Montefrío is a picturesque Andalusian town in southern Spain, renowned for its dramatic hilltop setting, whitewashed houses, and historic church and castle overlooking surrounding olive groves.
-
E.
Mazaleón
Mazaleón is a small municipality in the province of Teruel, Aragon, Spain, known for its rural character and location in the Matarranya comarca.
- F. None of above. chosen
Provenance (5 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_69e0b51d8a308190b09113b3b3f9bc15 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8afa1a9b0819083a5a8f34c49d409 |
completed | April 22, 2026, 11:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09ad65626c819081615b512241fe1c |
completed | May 17, 2026, 11:58 a.m. |
| NEDg | Description generation | batch_6a09b14b7694819084b4b391fa821961 |
completed | May 17, 2026, 12:15 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09b1af3e708190b7b2971db2508f7b |
completed | May 17, 2026, 12:16 p.m. |
Created at: April 16, 2026, 5:07 p.m.