Triple
T3746049
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Culver CityBus |
E81212
|
entity |
| Predicate | hasRoute |
P4374
|
FINISHED |
| Object |
Line 7
Line 7 is a Culver CityBus route in the Los Angeles area that provides local public transit service connecting key neighborhoods and transit hubs.
|
E384344
|
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 7 | Statement: [Culver CityBus, hasRoute, Line 7]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Line 7 Context triple: [Culver CityBus, hasRoute, Line 7]
-
A.
Line 7
Line 7 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 7
Line 7 is an east–west rapid transit line of the Beijing Subway serving several central and southwestern districts of Beijing.
-
C.
Line 7
Line 7 is a rapid transit line of the Guangzhou Metro system serving parts of Guangzhou and its surrounding areas.
-
D.
Line 7
Line 7 is one of the main lines of the Tehran Metro rapid transit network, serving various districts of Iran’s capital city.
-
E.
Line 7
Line 7 is a major rapid transit route of the Shanghai Metro that runs in a roughly north–south direction, connecting several key residential, commercial, and cultural areas across the city.
- 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 7 Triple: [Culver CityBus, hasRoute, Line 7]
Generated description
Line 7 is a Culver CityBus route in the Los Angeles area that provides local public transit service connecting key neighborhoods and transit hubs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Line 7 Target entity description: Line 7 is a Culver CityBus route in the Los Angeles area that provides local public transit service connecting key neighborhoods and transit hubs.
-
A.
Line 7
Line 7 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 7
Line 7 is a trolleybus route within Geneva’s public transport system that connects key districts of the city.
-
C.
Line 7
Line 7 is a major rapid transit route of the Shanghai Metro that runs in a roughly north–south direction, connecting several key residential, commercial, and cultural areas across the city.
-
D.
Line 7
Line 7 is a rapid transit line of the Guangzhou Metro system serving parts of Guangzhou and its surrounding areas.
-
E.
Line 7
Line 7 is one of the main lines of the Tehran Metro rapid transit network, serving various districts of Iran’s capital city.
- 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_69ad8b19b7b08190a6188804e99c53e9 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb680ddc819094205beb342699f9 |
completed | March 8, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4db2c2c5081909b83d89c989a8d1c |
completed | March 14, 2026, 3:51 a.m. |
| NEDg | Description generation | batch_69b4dbd0b6e88190a857afe3c1041788 |
completed | March 14, 2026, 3:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4dc5114ec8190aee92e21a48ae268 |
completed | March 14, 2026, 3:56 a.m. |
Created at: March 8, 2026, 3:35 p.m.