Triple
T15723703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vila Nova da Barquinha |
E381167
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Tomar |
E371690
|
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: Tomar | Statement: [Vila Nova da Barquinha, hasNearbyCity, Tomar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tomar Context triple: [Vila Nova da Barquinha, hasNearbyCity, Tomar]
-
A.
Tomar
chosen
Tomar is a historic Portuguese city in the Santarém District, best known for its Templar-founded Convent of Christ, a UNESCO World Heritage site.
-
B.
Valdemoro
Valdemoro is a municipality and growing suburban town in central Spain, located south of Madrid.
-
C.
Olmedo
Olmedo is a Spanish-language surname most notably associated with José Joaquín de Olmedo, an important Ecuadorian poet and statesman.
-
D.
Olmedo
Olmedo is a small town in the Gallura region of northern Sardinia, Italy, known for its rural character and traditional Sardinian culture.
-
E.
Madarihat
Madarihat is a small town in West Bengal, India, known primarily as the main gateway and service hub for visitors to Jaldapara National Park.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb1fdd4819088f3e243263e5f73 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82f68bf881909e5ad8a6ab81684a |
completed | May 9, 2026, 6:54 p.m. |
Created at: April 10, 2026, 4:46 a.m.