Triple
T17265487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pennsylvania Route 132 |
E419114
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Route 132
Route 132 is a state highway in Pennsylvania that serves as a key east–west arterial road through suburban communities.
|
E1261808
|
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: Route 132 | Statement: [Pennsylvania Route 132, alsoKnownAs, Route 132]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Route 132 Context triple: [Pennsylvania Route 132, alsoKnownAs, Route 132]
-
A.
Route 132
Route 132 is a major commercial roadway in Hyannis, Massachusetts, serving as a key access route to shopping centers and businesses on Cape Cod.
-
B.
Route 131
Route 131 is a provincial highway in Quebec, Canada, that serves as a key north–south transportation corridor through the Lanaudière region.
-
C.
Route 136
Route 136 is a state highway in southwestern Connecticut that runs through coastal communities and connects several local and regional routes.
-
D.
Route 136
Route 136 is an urban highway in Montreal that serves as a key east–west connector through the city’s central road network.
-
E.
Route 138
Route 138 is a major provincial highway in Quebec that runs along the north shore of the Saint Lawrence River, connecting numerous communities including Sainte-Anne-de-Beaupré.
- 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: Route 132 Triple: [Pennsylvania Route 132, alsoKnownAs, Route 132]
Generated description
Route 132 is a state highway in Pennsylvania that serves as a key east–west arterial road through suburban communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Route 132 Target entity description: Route 132 is a state highway in Pennsylvania that serves as a key east–west arterial road through suburban communities.
-
A.
Route 132
Route 132 is a major commercial roadway in Hyannis, Massachusetts, serving as a key access route to shopping centers and businesses on Cape Cod.
-
B.
Route 131
Route 131 is a provincial highway in Quebec, Canada, that serves as a key north–south transportation corridor through the Lanaudière region.
-
C.
Route 136
Route 136 is a state highway in southwestern Connecticut that runs through coastal communities and connects several local and regional routes.
-
D.
Route 136
Route 136 is an urban highway in Montreal that serves as a key east–west connector through the city’s central road network.
-
E.
Route 138
Route 138 is a major provincial highway in Quebec that runs along the north shore of the Saint Lawrence River, connecting numerous communities including Sainte-Anne-de-Beaupré.
- 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42f44ec7c81909a925fc8692b0a6c |
completed | April 19, 2026, 1:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180ce69d08190aa254f219a572a92 |
completed | May 11, 2026, 7:10 a.m. |
| NEDg | Description generation | batch_6a01850140388190949a5f613cef18c2 |
completed | May 11, 2026, 7:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a018563d38c819093df94b96b69cfd5 |
completed | May 11, 2026, 7:29 a.m. |
Created at: April 10, 2026, 5:40 a.m.