Triple
T15996330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 3 (Tunis Metro) |
E387974
|
entity |
| Predicate | lineDesignation |
P5539
|
FINISHED |
| Object |
Line 3
Line 3 is a light rail route of the Tunis Metro system in Tunis, Tunisia, serving key districts of the city as part of its urban public transport network.
|
E387974
|
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: Line 3 | Statement: [Line 3 (Tunis Metro), lineDesignation, Line 3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 3 Context triple: [Line 3 (Tunis Metro), lineDesignation, Line 3]
-
A.
Line 3
Line 3 is a route of Mexico City’s Metrobús bus rapid transit system that serves key corridors with dedicated lanes and high-capacity articulated buses.
-
B.
Line 3
Line 3 is a major rapid transit route of the Guangzhou Metro system, known for its high passenger volume and key role in connecting central urban areas with the airport and suburban districts.
-
C.
Line 3
Line 3 is a future rapid transit route of the Seville Metro intended to extend and improve the city’s urban rail network.
-
D.
Line 3
Line 3 is a major line of the Saint Petersburg Metro system, serving as one of the city's primary rapid transit routes.
-
E.
Line 3
Line 3 is a rapid transit line of the Shijiazhuang Metro system in Shijiazhuang, Hebei, China.
- 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: Line 3 Triple: [Line 3 (Tunis Metro), lineDesignation, Line 3]
Generated description
Line 3 is a light rail route of the Tunis Metro system in Tunis, Tunisia, serving key districts of the city as part of its urban public transport network.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 3 Target entity description: Line 3 is a light rail route of the Tunis Metro system in Tunis, Tunisia, serving key districts of the city as part of its urban public transport network.
-
A.
Line 3
chosen
Line 3 is one of the light rail routes of the Tunis Metro system, serving urban districts within the Tunis metropolitan area.
-
B.
Line 3
Line 3 is a major rapid transit route of the STC Metro system, serving key districts along its corridor.
-
C.
Line 3
Line 3 is a major north–south route of the Tehran Metro system, connecting key residential and commercial areas across the city.
-
D.
Line 3
Line 3 is one of the main lines of the Paris Métro, running in an east–west direction across the city and serving several central districts.
-
E.
Line 3
Line 3 is one of the main lines of the Barcelona Metro system, running through central parts of the city and connecting several key stations and neighborhoods.
- F. None of above.
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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1578709608190bae7bafa59280849 |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3d79dec8190b02e003f93e5dad6 |
completed | May 9, 2026, 11:31 p.m. |
| NEDg | Description generation | batch_69ffc50688ac8190b0911ce889254af3 |
completed | May 9, 2026, 11:36 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffc5902904819097a2c5efbde55882 |
completed | May 9, 2026, 11:38 p.m. |
Created at: April 10, 2026, 4:55 a.m.