Triple
T16643175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Teruel |
E404396
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Teruel |
E574615
|
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: Teruel | Statement: [Battle of Teruel, location, Teruel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Teruel Context triple: [Battle of Teruel, location, Teruel]
-
A.
Jaén
Jaén is a province in southern Spain’s Andalusia region, renowned for its vast olive groves and historic Renaissance towns.
-
B.
Jaén
Jaén is a significant commercial and agricultural city in northern Peru, known as a regional hub within the Cajamarca Region.
-
C.
Almería
Almería is a coastal city and province in southeastern Spain known for its arid climate, historic Alcazaba fortress, and extensive greenhouse agriculture.
-
D.
Zaragosa
Zaragosa is a barangay (village-level administrative division) within the municipality of Badian in the province of Cebu, Philippines.
-
E.
city of Teruel
chosen
The city of Teruel is the capital of Spain’s Teruel province, known for its Mudéjar architecture and historic medieval heritage.
- 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37ad27b5481908316b4f23fb8bc32 |
completed | April 18, 2026, 12:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00a50bcbe88190a424a361ee885b0c |
completed | May 10, 2026, 3:32 p.m. |
Created at: April 10, 2026, 5:18 a.m.