Triple
T12883183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ermesinde railway station |
E308154
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Ermesinde
Ermesinde is a town in northern Portugal, near Porto, known as a residential and transport hub within the Porto metropolitan area.
|
E1009593
|
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: Ermesinde | Statement: [Ermesinde railway station, locatedIn, Ermesinde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ermesinde Context triple: [Ermesinde railway station, locatedIn, Ermesinde]
-
A.
Alidoro
Alidoro is the wise philosopher and tutor to Prince Ramiro in Rossini’s opera "La Cenerentola," who secretly guides and protects Cinderella.
-
B.
Halistra
Halistra is a small crofting settlement on the Waternish peninsula of the Isle of Skye in Scotland.
-
C.
Lucciana
Lucciana is a commune in the Haute-Corse department of Corsica, France, known for hosting Bastia – Poretta Airport and its proximity to the island’s northeastern coast.
-
D.
Mora
Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
-
E.
Mora
Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
- 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: Ermesinde Triple: [Ermesinde railway station, locatedIn, Ermesinde]
Generated description
Ermesinde is a town in northern Portugal, near Porto, known as a residential and transport hub within the Porto metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ermesinde Target entity description: Ermesinde is a town in northern Portugal, near Porto, known as a residential and transport hub within the Porto metropolitan area.
-
A.
Alidoro
Alidoro is the wise philosopher and tutor to Prince Ramiro in Rossini’s opera "La Cenerentola," who secretly guides and protects Cinderella.
-
B.
Halistra
Halistra is a small crofting settlement on the Waternish peninsula of the Isle of Skye in Scotland.
-
C.
Lucciana
Lucciana is a commune in the Haute-Corse department of Corsica, France, known for hosting Bastia – Poretta Airport and its proximity to the island’s northeastern coast.
-
D.
Mora
Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
-
E.
Mora
Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d970fd15888190baf90fc30f2a3e25 |
completed | April 10, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a5556fe081909ada9d491b21b17b |
completed | May 3, 2026, 1:31 a.m. |
| NEDg | Description generation | batch_69f6a616f6e4819096c9850434882548 |
completed | May 3, 2026, 1:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f6a716bb2c81909dccc5ddbf3c92b5 |
completed | May 3, 2026, 1:38 a.m. |
Created at: April 9, 2026, 5:39 p.m.