Triple
T647734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Los Angeles Metro Rail |
E11277
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | El Segundo |
E52470
|
NE FINISHED |
How this triple was built (2 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: El Segundo | Statement: [Los Angeles Metro Rail, connectsTo, El Segundo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: El Segundo Context triple: [Los Angeles Metro Rail, connectsTo, El Segundo]
-
A.
El Segundo, California
chosen
El Segundo, California is a coastal city in Los Angeles County known for its concentration of aerospace, defense, and technology companies.
-
B.
Toa Baja
Toa Baja is a coastal municipality in northern Puerto Rico, known for its proximity to San Juan and its mix of urban, industrial, and residential areas.
-
C.
Doral
Doral is a discount cigarette brand produced by R.J. Reynolds Tobacco Company, known for its value-oriented positioning in the U.S. tobacco market.
-
D.
Rosarito
Rosarito is a coastal resort city in northern Baja California, Mexico, known for its beaches, tourism, and proximity to the U.S. border.
-
E.
San Fernando
San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f1cb24481909d3b41a56b29dee9 |
completed | March 1, 2026, 8:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a637452b0c8190a1e82989f20e68db |
completed | March 3, 2026, 1:20 a.m. |
Created at: March 1, 2026, 7:36 p.m.