Triple
T37300932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Houston–Dallas |
E925937
|
entity |
| Predicate | hasExistingUse |
P99647
|
FINISHED |
| Object | freight rail operations |
—
|
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: freight rail operations | Statement: [Houston–Dallas, hasExistingUse, freight rail operations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExistingUse Context triple: [Houston–Dallas, hasExistingUse, freight rail operations]
-
A.
hasPresentUse
chosen
Indicates that an entity is currently being used or serving a particular function at the present time.
-
B.
hasFormerUse
Indicates that something previously served a particular function or role that it no longer has.
-
C.
hasHumanUse
Indicates that something is used, employed, or utilized by humans for a particular purpose or benefit.
-
D.
hasHistoricalUsageIn
Indicates that something has been used or practiced within a particular historical period, context, or tradition.
-
E.
isInUse
Indicates that an entity is currently being utilized or actively engaged in its intended function or operation.
- F. None of above.
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_69f76eb1bc508190924e9fa5d8acdeb3 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd5bf69acc819092a01e4259785dc3 |
completed | May 8, 2026, 3:43 a.m. |
| PD | Predicate disambiguation | batch_69fd59b3f4ac8190a7f9dd3142da6e09 |
completed | May 8, 2026, 3:34 a.m. |
Created at: May 3, 2026, 4:16 p.m.