Triple
T2919673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kungsholmen |
E78688
|
entity |
| Predicate | hasNeighbourhood |
P4813
|
FINISHED |
| Object |
Stadshagen
Stadshagen is a residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
|
E527298
|
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: Stadshagen | Statement: [Kungsholmen, hasNeighbourhood, Stadshagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stadshagen Context triple: [Kungsholmen, hasNeighbourhood, Stadshagen]
-
A.
Sassenheim
Sassenheim is a town in the Dutch province of South Holland, known historically for its bulb-growing industry and location within the Duin- en Bollenstreek (Dune and Bulb Region).
-
B.
Heemstede
Heemstede is a town and municipality in the province of North Holland in the Netherlands, known as a leafy residential suburb near Haarlem.
-
C.
Werl
Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
-
D.
Veldhoven
Veldhoven is a town and municipality in the southern Netherlands, located near Eindhoven in the province of North Brabant.
-
E.
Hardinxveld-Giessendam
Hardinxveld-Giessendam is a Dutch town and municipality known for its shipbuilding industry and location along the river Merwede in the province of South Holland.
- 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: Stadshagen Triple: [Kungsholmen, hasNeighbourhood, Stadshagen]
Generated description
Stadshagen is a residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Stadshagen Target entity description: Stadshagen is a residential and commercial district on the island of Kungsholmen in central Stockholm, Sweden.
-
A.
Sassenheim
Sassenheim is a town in the Dutch province of South Holland, known historically for its bulb-growing industry and location within the Duin- en Bollenstreek (Dune and Bulb Region).
-
B.
Heemstede
Heemstede is a town and municipality in the province of North Holland in the Netherlands, known as a leafy residential suburb near Haarlem.
-
C.
Werl
Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
-
D.
Veldhoven
Veldhoven is a town and municipality in the southern Netherlands, located near Eindhoven in the province of North Brabant.
-
E.
Hardinxveld-Giessendam
Hardinxveld-Giessendam is a Dutch town and municipality known for its shipbuilding industry and location along the river Merwede in the province of South Holland.
- 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_69ad8b0c2ad081909ff87050ae542bb9 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad96a53f8c8190b188d549f1161e84 |
completed | March 8, 2026, 3:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfe31f64c88190b093e2bd65ae731b |
completed | March 22, 2026, 12:39 p.m. |
| NEDg | Description generation | batch_69bfe3ba92bc81908eee2923a2c8d16a |
completed | March 22, 2026, 12:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bfe4d171c48190b0f0011f0a668ad6 |
completed | March 22, 2026, 12:47 p.m. |
Created at: March 8, 2026, 2:54 p.m.