Triple
T4657821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hedmark |
E102451
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Tolga
Tolga is a small rural municipality in Innlandet county, Norway, known for its traditional farming landscape and mountainous surroundings.
|
E462702
|
NE FINISHED |
How this triple was built (4 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: Tolga | Statement: [Hedmark, hasMunicipality, Tolga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tolga Context triple: [Hedmark, hasMunicipality, Tolga]
-
A.
Tura
Tura is a prominent town in the Indian state of Meghalaya, serving as a major administrative, cultural, and economic center in the Garo Hills region.
-
B.
Tura
Tura is a district in southern Cairo, Egypt, historically known for its limestone quarries used in ancient Egyptian monuments.
-
C.
Dausa
Dausa is a town and district headquarters in the Indian state of Rajasthan, known for its historical forts, stepwells, and proximity to Jaipur.
-
D.
Tarchuna
Tarchuna is the ancient Etruscan city known in Latin as Tarquinii and in modern times as Tarquinia, a major cultural and political center of Etruria in central Italy.
-
E.
Tirico
Tirico is the surname of American sportscaster Mike Tirico, known for his play-by-play work on major sports broadcasts.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tolga Triple: [Hedmark, hasMunicipality, Tolga]
Generated description
Tolga is a small rural municipality in Innlandet county, Norway, known for its traditional farming landscape and mountainous surroundings.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tolga Target entity description: Tolga is a small rural municipality in Innlandet county, Norway, known for its traditional farming landscape and mountainous surroundings.
-
A.
Tura
Tura is a prominent town in the Indian state of Meghalaya, serving as a major administrative, cultural, and economic center in the Garo Hills region.
-
B.
Tura
Tura is a district in southern Cairo, Egypt, historically known for its limestone quarries used in ancient Egyptian monuments.
-
C.
Dausa
Dausa is a town and district headquarters in the Indian state of Rajasthan, known for its historical forts, stepwells, and proximity to Jaipur.
-
D.
Tarchuna
Tarchuna is the ancient Etruscan city known in Latin as Tarquinii and in modern times as Tarquinia, a major cultural and political center of Etruria in central Italy.
-
E.
Tirico
Tirico is the surname of American sportscaster Mike Tirico, known for his play-by-play work on major sports broadcasts.
- F. None of above. chosen
Provenance (5 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd631a855c81909773737fd238a14d |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be0378825881908fe3214f60be579e |
completed | March 21, 2026, 2:33 a.m. |
| NEDg | Description generation | batch_69be04c5549c819087204ac7e2e0e8ea |
completed | March 21, 2026, 2:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be05970dcc8190a86771d09f27d9f2 |
completed | March 21, 2026, 2:42 a.m. |
Created at: March 20, 2026, 1:15 p.m.