Triple
T1510398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tolima Department |
E31997
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Mariquita |
E97488
|
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: Mariquita | Statement: [Tolima Department, hasMajorCity, Mariquita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mariquita Context triple: [Tolima Department, hasMajorCity, Mariquita]
-
A.
Mariquita
chosen
Mariquita is a historic town in central Colombia known as an early colonial settlement and former mining center.
-
B.
Rosalinda
Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
-
C.
Tita de la Garza
Tita de la Garza is the passionate, emotionally expressive heroine of Laura Esquivel’s novel "Like Water for Chocolate," whose cooking magically channels her feelings.
-
D.
Marita
Marita is a feminine given name commonly used as a diminutive or affectionate form of the name Marie in various European languages.
-
E.
Mirta
Mirta is a feminine given name of Spanish origin commonly used in Spanish-speaking countries.
- 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_69a885e8caf88190a5fbb6159ce87786 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a907d4edd48190a03c85e1a0cc02b1 |
completed | March 5, 2026, 4:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad294805308190ada3ed69ec71ce43 |
completed | March 8, 2026, 7:46 a.m. |
Created at: March 4, 2026, 7:26 p.m.