Triple
T35234068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Highway 75 in Texas |
E1017320
|
entity |
| Predicate | roleInDallasArea |
P203177
|
FINISHED |
| Object | primary north–south freeway corridor |
—
|
LITERAL 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: primary north–south freeway corridor | Statement: [U.S. Highway 75 in Texas, roleInDallasArea, primary north–south freeway corridor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInDallasArea Context triple: [U.S. Highway 75 in Texas, roleInDallasArea, primary north–south freeway corridor]
-
A.
roleAtHouston
Indicates that an entity holds or held a specific role or position at an organization, institution, or context associated with Houston.
-
B.
roleInParisTexas
Indicates that an entity has a role or character part in the film "Paris, Texas."
-
C.
roleInPlanoReal
Indicates that an entity holds or held a specific role or function within the Plano Real economic plan or initiative.
-
D.
roleInSingapore
Indicates that an entity holds or has held a specific role, position, or function within the context of Singapore.
-
E.
roleInIndustry
Indicates the specific function, position, or capacity an entity holds within a particular industry or sector.
- F. None of above. chosen
Provenance (4 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_69f76de12e4c8190bc46b71a32858356 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a013632a3048190b05716d803f34716 |
completed | May 11, 2026, 1:51 a.m. |
| PD | Predicate disambiguation | batch_6a01309582e48190a05d47d96ffb7a46 |
completed | May 11, 2026, 1:27 a.m. |
| PDg | Predicate description generation | batch_6a013631f4d48190b1e79e8ff313d3d2 |
completed | May 11, 2026, 1:51 a.m. |
Created at: May 3, 2026, 4:02 p.m.