Triple
T3137950
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manabí Province |
E65578
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Manta |
E241444
|
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: Manta | Statement: [Manabí Province, containsCity, Manta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Manta Context triple: [Manabí Province, containsCity, Manta]
-
A.
Manta
chosen
Manta is a major coastal city and important seaport in western Ecuador, known for its fishing industry, beaches, and commercial activity.
-
B.
Mola
Mola is a Spanish surname most notably associated with Emilio Mola, a key Nationalist general during the Spanish Civil War.
-
C.
Tayassu
Tayassu is a genus of New World peccaries, medium-sized pig-like mammals native to Central and South American forests and scrublands.
-
D.
Pantar
Pantar is an island in eastern Indonesia’s Alor archipelago, known for its linguistic diversity and use of several Central Malayo-Polynesian languages.
-
E.
Tiburon
Tiburon is a small, affluent waterfront town in Marin County, California, known for its scenic views of San Francisco Bay and ferry access to nearby islands.
- 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_69ad8581c25c8190b0d85ba9b9baa531 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada574509c81908a88bb10ea35516d |
completed | March 8, 2026, 4:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f8a1a2081909081c36075d4ddbe |
completed | March 12, 2026, 12:57 a.m. |
Created at: March 8, 2026, 3:05 p.m.