Triple
T28120577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilder Avenue |
E710771
|
entity |
| Predicate | hasIntersectionWith |
P13379
|
FINISHED |
| Object |
Makiki Street
Makiki Street is a roadway in Honolulu, Hawaii, running through the Makiki neighborhood and connecting residential areas with nearby urban thoroughfares.
|
E1807169
|
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: Makiki Street | Statement: [Wilder Avenue, hasIntersectionWith, Makiki Street]
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: Makiki Street Triple: [Wilder Avenue, hasIntersectionWith, Makiki Street]
Generated description
Makiki Street is a roadway in Honolulu, Hawaii, running through the Makiki neighborhood and connecting residential areas with nearby urban thoroughfares.
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_69ef9b72f63081909dfbc2c1ddae86c6 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f640f73d9481909a07574a7db092eb |
completed | May 2, 2026, 6:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a15e69d39648190b1103a6496c453bc |
completed | May 26, 2026, 6:29 p.m. |
| NEDg | Description generation | batch_6a15e76bf13c819086a74eb45905fe8a |
completed | May 26, 2026, 6:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a15e7f45adc819089637dc508d50a21 |
completed | May 26, 2026, 6:35 p.m. |
Created at: April 27, 2026, 9:16 p.m.