Triple
T15568149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ansião |
E374168
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Penela |
E374183
|
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: Penela | Statement: [Ansião, borders, Penela]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Penela Context triple: [Ansião, borders, Penela]
-
A.
Penela
chosen
Penela is a historic municipality in central Portugal known for its medieval castle and scenic location within the Coimbra District.
-
B.
Parea
Parea is a small coastal village on the island of Huahine in French Polynesia, known for its tranquil beaches and traditional Polynesian atmosphere.
-
C.
Peren
Peren is a town and administrative center in the northeastern Indian state of Nagaland.
-
D.
Pankshin
Pankshin is a town and local government area in central Nigeria known as an administrative and educational center within Plateau State.
-
E.
Nolana
Nolana is a genus of flowering plants native mainly to coastal regions of South America, known for their showy, often blue, funnel-shaped blossoms.
- 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04dde90b081908284d9258d4462e3 |
completed | April 16, 2026, 2:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff4c4219a081909acca9f783ecd44b |
completed | May 9, 2026, 3:01 p.m. |
Created at: April 10, 2026, 4:10 a.m.