Triple
T15785254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Expo/Bundy station |
E382721
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object |
EXPO/BUNDY
EXPO/BUNDY is the station code for Expo/Bundy station, a light rail stop on the Los Angeles Metro E Line in West Los Angeles.
|
E1176567
|
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: EXPO/BUNDY | Statement: [Expo/Bundy station, hasStationCode, EXPO/BUNDY]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: EXPO/BUNDY Context triple: [Expo/Bundy station, hasStationCode, EXPO/BUNDY]
-
A.
Expo
Expo is an open-source platform and toolchain for building, deploying, and iterating on React Native applications.
-
B.
Expo
Expo is a popular brand best known for its dry-erase markers and related whiteboard accessories commonly used in schools, offices, and homes.
-
C.
Expa
Expa is a startup studio and venture firm created by entrepreneur Garrett Camp to build and support early-stage technology companies.
-
D.
The Expo
The Expo is a well-known multipurpose event and exhibition venue in Portland, Oregon, hosting trade shows, conventions, and community events.
-
E.
EXPE
EXPE is the stock ticker symbol for Expedia Group, a major American online travel company that operates brands like Expedia.com, Hotels.com, and Vrbo.
- 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: EXPO/BUNDY Triple: [Expo/Bundy station, hasStationCode, EXPO/BUNDY]
Generated description
EXPO/BUNDY is the station code for Expo/Bundy station, a light rail stop on the Los Angeles Metro E Line in West Los Angeles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: EXPO/BUNDY Target entity description: EXPO/BUNDY is the station code for Expo/Bundy station, a light rail stop on the Los Angeles Metro E Line in West Los Angeles.
-
A.
Expo
Expo is an open-source platform and toolchain for building, deploying, and iterating on React Native applications.
-
B.
Expo
Expo is a popular brand best known for its dry-erase markers and related whiteboard accessories commonly used in schools, offices, and homes.
-
C.
Expa
Expa is a startup studio and venture firm created by entrepreneur Garrett Camp to build and support early-stage technology companies.
-
D.
The Expo
The Expo is a well-known multipurpose event and exhibition venue in Portland, Oregon, hosting trade shows, conventions, and community events.
-
E.
EXPE
EXPE is the stock ticker symbol for Expedia Group, a major American online travel company that operates brands like Expedia.com, Hotels.com, and Vrbo.
- 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_69d86da16e188190b89af699f1ed0bfe |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05401c4788190a31c180953433db9 |
completed | April 16, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff90a4661481909d04bcb9f5043a6b |
completed | May 9, 2026, 7:53 p.m. |
| NEDg | Description generation | batch_69ff935867d08190955c2d665a761a5e |
completed | May 9, 2026, 8:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff93fc19048190981d8e44ee5222f7 |
completed | May 9, 2026, 8:07 p.m. |
Created at: April 10, 2026, 4:48 a.m.