Triple
T38616346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Morales |
E936729
|
entity |
| Predicate | vehicleModification |
P191869
|
FINISHED |
| Object | heavily modified taxi |
—
|
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: heavily modified taxi | Statement: [Daniel Morales, vehicleModification, heavily modified taxi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleModification Context triple: [Daniel Morales, vehicleModification, heavily modified taxi]
-
A.
aircraftModification
Indicates a relationship where an aircraft undergoes a change, upgrade, or alteration to its structure, systems, or configuration.
-
B.
aftermarketAccessories
Indicates that one entity provides or is associated with accessories added to a product after its original manufacture or sale.
-
C.
vehicleApplication
Indicates that an application, request, or process is specifically associated with or intended for a vehicle.
-
D.
vehicleBase
Indicates that one entity serves as the foundational or underlying base for a vehicle-related entity or system.
-
E.
vehicleVariant
Indicates that one vehicle is a specific version, model, or configuration variant of another related vehicle.
- 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_69f76ed403208190b862dc795171353f |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcec5f8b448190b48330a19b462d24 |
completed | May 7, 2026, 7:47 p.m. |
| PD | Predicate disambiguation | batch_69fceaf1e23881908ca24160a638e329 |
completed | May 7, 2026, 7:41 p.m. |
| PDg | Predicate description generation | batch_69fcec5e560481909cd710b88897e833 |
completed | May 7, 2026, 7:47 p.m. |
Created at: May 3, 2026, 4:32 p.m.