Triple
T15365524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lingotto (Turin Metro) |
E367402
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
LING
LING is the station code for Lingotto, a Turin Metro station serving the Lingotto district in Turin, Italy.
|
E1153416
|
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: LING | Statement: [Lingotto (Turin Metro), hasStationCode, LING]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LING Context triple: [Lingotto (Turin Metro), hasStationCode, LING]
-
A.
Lang
Lang is the given name of the renowned Chinese concert pianist Lang Lang, celebrated for his virtuosic technique and charismatic performances.
-
B.
Lang
Lang is a common Scottish surname borne by numerous notable figures across literature, politics, and other fields.
-
C.
Lint
Lint is a small municipality in the Belgian province of Antwerp, known for its residential character and proximity to the city of Antwerp.
-
D.
lint
lint is a static code analysis tool that detects potential errors, bugs, and style issues in C source code before compilation or execution.
-
E.
LEX
LEX is the abbreviation for the Léman Express, a cross-border commuter rail network serving the Greater Geneva region in Switzerland and France.
- 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: LING Triple: [Lingotto (Turin Metro), hasStationCode, LING]
Generated description
LING is the station code for Lingotto, a Turin Metro station serving the Lingotto district in Turin, Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: LING Target entity description: LING is the station code for Lingotto, a Turin Metro station serving the Lingotto district in Turin, Italy.
-
A.
Lang
Lang is a common Scottish surname borne by numerous notable figures across literature, politics, and other fields.
-
B.
Lang
Lang is the given name of the renowned Chinese concert pianist Lang Lang, celebrated for his virtuosic technique and charismatic performances.
-
C.
Lint
Lint is a small municipality in the Belgian province of Antwerp, known for its residential character and proximity to the city of Antwerp.
-
D.
lint
lint is a static code analysis tool that detects potential errors, bugs, and style issues in C source code before compilation or execution.
-
E.
LEX
LEX is the abbreviation for the Léman Express, a cross-border commuter rail network serving the Greater Geneva region in Switzerland and France.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e497de48190be249b110999ec5c |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff0b4cc39c81908a0aff959352f6d5 |
completed | May 9, 2026, 10:24 a.m. |
| NEDg | Description generation | batch_69ff0df908848190b05c2ecf64f10b08 |
completed | May 9, 2026, 10:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff0e8f3b4481909642c91f1a54843c |
completed | May 9, 2026, 10:38 a.m. |
Created at: April 10, 2026, 3:18 a.m.